household_synthesizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import sklearn.preprocessing as skpre\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import time\n",
    "\n",
    "import DataProcessor\n",
    "import srmse\n",
    "import vae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the persons PUMS dataset for WA state\n",
    "h_df = pd.read_csv(\"data/PUMS_2017_5YR_WA/psam_h53.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose PUMS and Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset n=164836 pre-cleaning\n",
      "Dataset n=140586 post-cleaning\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HINCP</th>\n",
       "      <th>NP</th>\n",
       "      <th>VEH</th>\n",
       "      <th>BLD</th>\n",
       "      <th>ACCESS</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>140586 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HINCP  NP  VEH  BLD  ACCESS\n",
       "0         7.0   2  2.0  2.0     1.0\n",
       "1         5.0   2  2.0  5.0     1.0\n",
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       "\n",
       "[140586 rows x 5 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to desired variables (numeric then categorical)\n",
    "pums_data = h_df[['WGTP','HINCP','NP','VEH','BLD','ACCESS']].copy()\n",
    "\n",
    "# Convert numerical variables to categorical bins\n",
    "pums_data['HINCP'], hincp_bins = pd.qcut(pums_data['HINCP'], q=10, labels=False, retbins=True)\n",
    "\n",
    "# Remove NA values and check n before/after\n",
    "print(f\"Dataset n={len(pums_data)} pre-cleaning\")\n",
    "pums_data = pums_data.dropna()\n",
    "print(f\"Dataset n={len(pums_data)} post-cleaning\")\n",
    "\n",
    "# Scale by the household-weights to get true population\n",
    "# pums_data = pums_data.reindex(pums_data.index.repeat(pums_data['WGTP']))\n",
    "pums_data = pums_data.drop('WGTP', axis=1)\n",
    "# pums_data = pums_data.sample(150000)\n",
    "pums_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "VAR_DIM = pums_data.shape[1]\n",
    "VAR_NAMES = pums_data.columns\n",
    "\n",
    "# Split categorical data into OHE vars, save num classes per variable\n",
    "dummies_list = []\n",
    "pums_encodings_list = []\n",
    "for x in range(VAR_DIM):\n",
    "    dummies = pums_data.iloc[:,x]\n",
    "    pums_encodings_list.append(np.unique(dummies.values))\n",
    "    dummies = pd.get_dummies(dummies, prefix=f\"{pums_data.columns[x]}_\")\n",
    "    dummies_list.append(dummies)\n",
    "CAT_LENGTHS = [x.shape[1] for x in dummies_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "        HINCP__0.0  HINCP__1.0  HINCP__2.0  HINCP__3.0  HINCP__4.0  \\\n",
       "0                0           0           0           0           0   \n",
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       "...            ...         ...         ...         ...         ...   \n",
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       "\n",
       "        HINCP__5.0  HINCP__6.0  HINCP__7.0  HINCP__8.0  HINCP__9.0  ...  \\\n",
       "0                0           0           1           0           0  ...   \n",
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       "161776           0           0           1           0           0  ...   \n",
       "\n",
       "        BLD__4.0  BLD__5.0  BLD__6.0  BLD__7.0  BLD__8.0  BLD__9.0  BLD__10.0  \\\n",
       "0              0         0         0         0         0         0          0   \n",
       "1              0         1         0         0         0         0          0   \n",
       "2              0         0         0         0         0         0          0   \n",
       "3              0         0         0         0         0         0          0   \n",
       "4              0         0         0         0         0         0          0   \n",
       "...          ...       ...       ...       ...       ...       ...        ...   \n",
       "161771         0         0         0         0         0         0          0   \n",
       "161772         0         0         0         0         0         0          0   \n",
       "161774         0         0         0         0         0         0          0   \n",
       "161775         0         0         0         0         0         0          0   \n",
       "161776         0         0         0         0         0         0          0   \n",
       "\n",
       "        ACCESS__1.0  ACCESS__2.0  ACCESS__3.0  \n",
       "0                 1            0            0  \n",
       "1                 1            0            0  \n",
       "2                 1            0            0  \n",
       "3                 0            0            1  \n",
       "4                 1            0            0  \n",
       "...             ...          ...          ...  \n",
       "161771            1            0            0  \n",
       "161772            1            0            0  \n",
       "161774            1            0            0  \n",
       "161775            1            0            0  \n",
       "161776            1            0            0  \n",
       "\n",
       "[140586 rows x 48 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final data frame after encoding OHE\n",
    "model_data_df = pd.DataFrame()\n",
    "for ohe_var in dummies_list:\n",
    "    model_data_df = pd.concat([model_data_df, ohe_var], axis=1)\n",
    "MANIFEST_DIM = model_data_df.shape[1]\n",
    "\n",
    "# Preview data that will be fed into model\n",
    "model_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on 98410 samples\n"
     ]
    }
   ],
   "source": [
    "# Separate into train/test data\n",
    "model_data = model_data_df.values\n",
    "train_idx = round(len(model_data)*.7)\n",
    "train_data = model_data[0:train_idx,:]\n",
    "test_data = model_data[train_idx:len(model_data),:]\n",
    "\n",
    "print(f\"Training on {train_idx} samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Parameters and Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search SRMSE 1\n",
    "# BATCH_SIZES = [64, 128, 256, 512, 1024]\n",
    "# EPOCHS = [1000]\n",
    "# LATENT_DIMS = [2, 3, 4]\n",
    "# HIDDEN_DIMS = [8, 16, 32, 64]\n",
    "# KL_WEIGHTS = [.01, .05, .1, 1.0]\n",
    "# [155.67232656478882, 1.8501038551330566, 0.05296959012880786, 0.02081236519716125, [128, 1000, 2, 32, 0.05]]\n",
    "# [98.54442024230957, 1.7626943588256836, 0.06421160670330948, 0.020779257538261826, [256, 1000, 4, 32, 0.1]]\n",
    "# [44.24084186553955, 2.3369486331939697, 0.06478558327639009, 0.02501131310084622, [1024, 1000, 4, 16, 0.1]]\n",
    "\n",
    "# Hyperparameters\n",
    "BATCH_SIZE = 1024\n",
    "EPOCHS = 1000\n",
    "LATENT_DIM = 4\n",
    "HIDDEN_DIM = 16\n",
    "KL_WEIGHT = .05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_loss = []\n",
    "val_epochs = []\n",
    "class validationCallback(keras.callbacks.Callback):\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        if epoch % 10 == 0:\n",
    "            val_epochs.append(epoch)\n",
    "            # Use random sample of 1000 from test data to validate the model\n",
    "            idx = np.random.randint(test_data.shape[0], size=1000)\n",
    "            val_data = test_data[idx]\n",
    "            results = vae_model.predict(val_data)\n",
    "            loss_cat = vae.get_reconstruction_loss(val_data, results, CAT_LENGTHS)\n",
    "            val_loss.append(float(loss_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testModel(test_vae, latent_dim):\n",
    "    # How many synthetic households to generate\n",
    "    POP_SIZE = 100000  # Number of hh in WA\n",
    "\n",
    "    # Generate random normal sample to represent each latent variable, for each row (different hh per row)\n",
    "    np.random.seed(42)\n",
    "    inputs = np.random.normal(loc=0, scale=1, size=(POP_SIZE, latent_dim))\n",
    "\n",
    "    start_time = time.time()\n",
    "    # Generate households; each hh has unique latent input\n",
    "    results = test_vae.decoder.predict([inputs, inputs[:,latent_dim:]])  # Nothing is actually in the second input\n",
    "    gen_time = time.time() - start_time\n",
    "\n",
    "    synth_hh_df = pd.DataFrame()\n",
    "    for i, x in enumerate(results[0]):\n",
    "        result = np.apply_along_axis(DataProcessor.logit_to_val, 1, x, pums_encodings_list[i])\n",
    "        result_df = pd.DataFrame(result).add_prefix(f\"{VAR_NAMES[i]}_\")\n",
    "        synth_hh_df = pd.concat([synth_hh_df, result_df], axis=1)\n",
    "    synth_hh_df.columns = VAR_NAMES\n",
    "    \n",
    "    # Get univariate and bivariate srmse for the current model\n",
    "    srmse_uni = srmse.calculateSRMSE(synth_hh_df, pums_data.sample(POP_SIZE, random_state=42))\n",
    "    srmse_bi = srmse.calculateBivariateSRMSE(synth_hh_df, pums_data.sample(POP_SIZE, random_state=42))\n",
    "\n",
    "    return gen_time, srmse_uni, srmse_bi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# HH_IDX = len(CAT_LENGTHS)  # Number of household variables (used in CVAE)\n",
    "\n",
    "# # Use grid search to train model and get results for many different hyperparameters\n",
    "# grid_search_results = []\n",
    "# for bs in BATCH_SIZES:\n",
    "#     for e in EPOCHS:\n",
    "#         for ld in LATENT_DIMS:\n",
    "#             for hd in HIDDEN_DIMS:\n",
    "#                 for klw in KL_WEIGHTS:\n",
    "#                     print(f\"[{bs}, {e}, {ld}, {hd}, {klw}]\")\n",
    "#                     start_time = time.time()\n",
    "#                     vae = VAE.VAE(MANIFEST_DIM, hd, ld, CAT_LENGTHS, HH_IDX, klw)\n",
    "#                     vae.compile(optimizer=keras.optimizers.Adam())\n",
    "#                     history = vae.fit(train_data, epochs=e, batch_size=bs, callbacks=[validationCallback()], verbose=0)\n",
    "#                     train_time = (time.time()-start_time)\n",
    "#                     gen_time, srmse_uni, srmse_bi = testModel(test_vae=vae, latent_dim=ld)\n",
    "#                     results = [train_time, gen_time, srmse_uni, srmse_bi, [bs,e,ld,hd,klw]]\n",
    "#                     grid_search_results.append(results)\n",
    "\n",
    "# # Find the best results for each metric in the grid search\n",
    "# min_srmse_uni_idx = 0\n",
    "# min_srmse_uni_value = 999999999\n",
    "# min_srmse_bi_idx = 0\n",
    "# min_srmse_bi_value = 999999999\n",
    "# min_eff_idx = 0\n",
    "# min_eff_value = 999999999\n",
    "# for i, result in enumerate(grid_search_results):\n",
    "#     tot_time = result[0]+result[1]\n",
    "#     time_eff = (result[2]+result[3] / 2) * tot_time\n",
    "#     if result[2] < min_srmse_uni_value:\n",
    "#         min_srmse_uni_idx = i\n",
    "#         min_srmse_uni_value = result[2]\n",
    "#     if result[3] < min_srmse_bi_value:\n",
    "#         min_srmse_bi_idx = i\n",
    "#         min_srmse_bi_value = result[3]\n",
    "#     if time_eff < min_eff_value:\n",
    "#         min_eff_idx = i\n",
    "#         min_eff_value = time_eff\n",
    "\n",
    "# # Display the results\n",
    "# print(grid_search_results[min_srmse_uni_idx])\n",
    "# print(grid_search_results[min_srmse_bi_idx])\n",
    "# print(grid_search_results[min_eff_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HH_IDX = len(CAT_LENGTHS)  # Number of household variables (used in CVAE)\n",
    "HH_IDX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n",
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-20 11:52:28.606699: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-12-20 11:52:28.606776: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2021-12-20 11:52:28.733223: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2021-12-20 11:52:29.027687: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - ETA: 0s - tot_loss: 8.8305 - rec_loss: 8.7081 - kl_loss: 0.1224"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-20 11:52:31.229937: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 3s 24ms/step - tot_loss: 8.8305 - rec_loss: 8.7081 - kl_loss: 0.1224\n",
      "Epoch 2/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 6.6695 - rec_loss: 6.4670 - kl_loss: 0.2025\n",
      "Epoch 3/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 5.9527 - rec_loss: 5.7367 - kl_loss: 0.2161\n",
      "Epoch 4/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 5.4794 - rec_loss: 5.2507 - kl_loss: 0.2288\n",
      "Epoch 5/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 4.9639 - rec_loss: 4.6780 - kl_loss: 0.2860\n",
      "Epoch 6/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 4.6473 - rec_loss: 4.3190 - kl_loss: 0.3283\n",
      "Epoch 7/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 4.4684 - rec_loss: 4.1229 - kl_loss: 0.3455\n",
      "Epoch 8/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 4.3338 - rec_loss: 3.9779 - kl_loss: 0.3559\n",
      "Epoch 9/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 4.2022 - rec_loss: 3.8362 - kl_loss: 0.3661\n",
      "Epoch 10/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 4.0437 - rec_loss: 3.6663 - kl_loss: 0.3774\n",
      "Epoch 11/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 3.8631 - rec_loss: 3.4725 - kl_loss: 0.3907\n",
      "Epoch 12/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.6980 - rec_loss: 3.2957 - kl_loss: 0.4023\n",
      "Epoch 13/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.5460 - rec_loss: 3.1353 - kl_loss: 0.4106\n",
      "Epoch 14/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.4092 - rec_loss: 2.9921 - kl_loss: 0.4171\n",
      "Epoch 15/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.2804 - rec_loss: 2.8570 - kl_loss: 0.4233\n",
      "Epoch 16/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.1707 - rec_loss: 2.7421 - kl_loss: 0.4286\n",
      "Epoch 17/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 3.0575 - rec_loss: 2.6237 - kl_loss: 0.4337\n",
      "Epoch 18/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.9525 - rec_loss: 2.5139 - kl_loss: 0.4386\n",
      "Epoch 19/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.8530 - rec_loss: 2.4102 - kl_loss: 0.4428\n",
      "Epoch 20/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.7623 - rec_loss: 2.3137 - kl_loss: 0.4486\n",
      "Epoch 21/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 2.6775 - rec_loss: 2.2240 - kl_loss: 0.4535\n",
      "Epoch 22/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 2.5906 - rec_loss: 2.1329 - kl_loss: 0.4577\n",
      "Epoch 23/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 2.5149 - rec_loss: 2.0524 - kl_loss: 0.4625\n",
      "Epoch 24/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.4446 - rec_loss: 1.9797 - kl_loss: 0.4649\n",
      "Epoch 25/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.3814 - rec_loss: 1.9130 - kl_loss: 0.4684\n",
      "Epoch 26/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 2.3180 - rec_loss: 1.8479 - kl_loss: 0.4702\n",
      "Epoch 27/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.2641 - rec_loss: 1.7914 - kl_loss: 0.4726\n",
      "Epoch 28/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.2094 - rec_loss: 1.7359 - kl_loss: 0.4735\n",
      "Epoch 29/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.1671 - rec_loss: 1.6914 - kl_loss: 0.4758\n",
      "Epoch 30/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.1198 - rec_loss: 1.6430 - kl_loss: 0.4768\n",
      "Epoch 31/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 2.0783 - rec_loss: 1.6008 - kl_loss: 0.4775\n",
      "Epoch 32/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 2.0345 - rec_loss: 1.5567 - kl_loss: 0.4778\n",
      "Epoch 33/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.9959 - rec_loss: 1.5167 - kl_loss: 0.4792\n",
      "Epoch 34/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.9553 - rec_loss: 1.4757 - kl_loss: 0.4796\n",
      "Epoch 35/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.9224 - rec_loss: 1.4430 - kl_loss: 0.4793\n",
      "Epoch 36/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.8902 - rec_loss: 1.4101 - kl_loss: 0.4801\n",
      "Epoch 37/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.8552 - rec_loss: 1.3755 - kl_loss: 0.4796\n",
      "Epoch 38/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.8278 - rec_loss: 1.3479 - kl_loss: 0.4800\n",
      "Epoch 39/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.7987 - rec_loss: 1.3185 - kl_loss: 0.4802\n",
      "Epoch 40/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.7714 - rec_loss: 1.2920 - kl_loss: 0.4794\n",
      "Epoch 41/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.7487 - rec_loss: 1.2687 - kl_loss: 0.4801\n",
      "Epoch 42/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.7221 - rec_loss: 1.2421 - kl_loss: 0.4800\n",
      "Epoch 43/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6987 - rec_loss: 1.2185 - kl_loss: 0.4802\n",
      "Epoch 44/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6771 - rec_loss: 1.1961 - kl_loss: 0.4810\n",
      "Epoch 45/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6588 - rec_loss: 1.1776 - kl_loss: 0.4812\n",
      "Epoch 46/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6357 - rec_loss: 1.1540 - kl_loss: 0.4817\n",
      "Epoch 47/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6230 - rec_loss: 1.1416 - kl_loss: 0.4814\n",
      "Epoch 48/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.6011 - rec_loss: 1.1193 - kl_loss: 0.4817\n",
      "Epoch 49/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5844 - rec_loss: 1.1022 - kl_loss: 0.4822\n",
      "Epoch 50/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5695 - rec_loss: 1.0870 - kl_loss: 0.4825\n",
      "Epoch 51/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.5568 - rec_loss: 1.0742 - kl_loss: 0.4827\n",
      "Epoch 52/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5426 - rec_loss: 1.0604 - kl_loss: 0.4822\n",
      "Epoch 53/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5295 - rec_loss: 1.0467 - kl_loss: 0.4829\n",
      "Epoch 54/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5172 - rec_loss: 1.0340 - kl_loss: 0.4832\n",
      "Epoch 55/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.5050 - rec_loss: 1.0201 - kl_loss: 0.4849\n",
      "Epoch 56/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4946 - rec_loss: 1.0099 - kl_loss: 0.4847\n",
      "Epoch 57/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4854 - rec_loss: 1.0009 - kl_loss: 0.4845\n",
      "Epoch 58/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4722 - rec_loss: 0.9872 - kl_loss: 0.4850\n",
      "Epoch 59/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4631 - rec_loss: 0.9772 - kl_loss: 0.4859\n",
      "Epoch 60/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4532 - rec_loss: 0.9676 - kl_loss: 0.4856\n",
      "Epoch 61/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.4446 - rec_loss: 0.9581 - kl_loss: 0.4866\n",
      "Epoch 62/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4312 - rec_loss: 0.9446 - kl_loss: 0.4866\n",
      "Epoch 63/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4252 - rec_loss: 0.9380 - kl_loss: 0.4873\n",
      "Epoch 64/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4147 - rec_loss: 0.9283 - kl_loss: 0.4864\n",
      "Epoch 65/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.4051 - rec_loss: 0.9184 - kl_loss: 0.4867\n",
      "Epoch 66/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.3966 - rec_loss: 0.9112 - kl_loss: 0.4854\n",
      "Epoch 67/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.3911 - rec_loss: 0.9032 - kl_loss: 0.4879\n",
      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
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      "Epoch 70/1000\n",
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      "Epoch 71/1000\n",
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      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
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      "Epoch 75/1000\n",
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      "Epoch 76/1000\n",
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      "Epoch 77/1000\n",
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      "Epoch 78/1000\n",
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      "Epoch 79/1000\n",
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      "Epoch 80/1000\n",
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      "Epoch 81/1000\n",
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      "Epoch 82/1000\n",
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      "Epoch 83/1000\n",
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      "Epoch 84/1000\n",
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      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
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      "Epoch 87/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.2401 - rec_loss: 0.7469 - kl_loss: 0.4932\n",
      "Epoch 88/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.2340 - rec_loss: 0.7416 - kl_loss: 0.4924\n",
      "Epoch 89/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.2288 - rec_loss: 0.7350 - kl_loss: 0.4938\n",
      "Epoch 90/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.2201 - rec_loss: 0.7268 - kl_loss: 0.4933\n",
      "Epoch 91/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.2132 - rec_loss: 0.7196 - kl_loss: 0.4936\n",
      "Epoch 92/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.2109 - rec_loss: 0.7156 - kl_loss: 0.4954\n",
      "Epoch 93/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.2007 - rec_loss: 0.7072 - kl_loss: 0.4935\n",
      "Epoch 94/1000\n",
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      "Epoch 95/1000\n",
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      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.1824 - rec_loss: 0.6878 - kl_loss: 0.4946\n",
      "Epoch 98/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.1718 - rec_loss: 0.6790 - kl_loss: 0.4928\n",
      "Epoch 99/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.1682 - rec_loss: 0.6743 - kl_loss: 0.4938\n",
      "Epoch 100/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.1686 - rec_loss: 0.6736 - kl_loss: 0.4950\n",
      "Epoch 101/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.1634 - rec_loss: 0.6681 - kl_loss: 0.4953\n",
      "Epoch 102/1000\n",
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      "Epoch 103/1000\n",
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      "Epoch 104/1000\n",
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      "Epoch 105/1000\n",
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      "Epoch 106/1000\n",
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      "Epoch 107/1000\n",
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      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 1.1219 - rec_loss: 0.6254 - kl_loss: 0.4965\n",
      "Epoch 112/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.1079 - rec_loss: 0.6118 - kl_loss: 0.4962\n",
      "Epoch 116/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.1030 - rec_loss: 0.6068 - kl_loss: 0.4962\n",
      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0995 - rec_loss: 0.6025 - kl_loss: 0.4969\n",
      "Epoch 119/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.0951 - rec_loss: 0.5987 - kl_loss: 0.4964\n",
      "Epoch 120/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.0922 - rec_loss: 0.5946 - kl_loss: 0.4976\n",
      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
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      "Epoch 123/1000\n",
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      "Epoch 124/1000\n",
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      "Epoch 125/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 1.0766 - rec_loss: 0.5786 - kl_loss: 0.4980\n",
      "Epoch 126/1000\n",
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      "Epoch 127/1000\n",
      "97/97 [==============================] - 2s 25ms/step - tot_loss: 1.0689 - rec_loss: 0.5710 - kl_loss: 0.4979\n",
      "Epoch 128/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.0673 - rec_loss: 0.5697 - kl_loss: 0.4976\n",
      "Epoch 129/1000\n",
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      "Epoch 130/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0608 - rec_loss: 0.5633 - kl_loss: 0.4975\n",
      "Epoch 131/1000\n",
      "97/97 [==============================] - 2s 23ms/step - tot_loss: 1.0575 - rec_loss: 0.5582 - kl_loss: 0.4993\n",
      "Epoch 132/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0565 - rec_loss: 0.5571 - kl_loss: 0.4994\n",
      "Epoch 133/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0518 - rec_loss: 0.5527 - kl_loss: 0.4991\n",
      "Epoch 134/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0495 - rec_loss: 0.5497 - kl_loss: 0.4997\n",
      "Epoch 135/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0468 - rec_loss: 0.5476 - kl_loss: 0.4991\n",
      "Epoch 136/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0449 - rec_loss: 0.5442 - kl_loss: 0.5007\n",
      "Epoch 137/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0412 - rec_loss: 0.5409 - kl_loss: 0.5004\n",
      "Epoch 138/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0380 - rec_loss: 0.5377 - kl_loss: 0.5003\n",
      "Epoch 139/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0375 - rec_loss: 0.5373 - kl_loss: 0.5001\n",
      "Epoch 140/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0350 - rec_loss: 0.5339 - kl_loss: 0.5011\n",
      "Epoch 141/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 1.0313 - rec_loss: 0.5296 - kl_loss: 0.5017\n",
      "Epoch 142/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0320 - rec_loss: 0.5307 - kl_loss: 0.5013\n",
      "Epoch 143/1000\n",
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      "Epoch 144/1000\n",
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      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 1.0186 - rec_loss: 0.5168 - kl_loss: 0.5018\n",
      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
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      "Epoch 150/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 152/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9981 - rec_loss: 0.4953 - kl_loss: 0.5028\n",
      "Epoch 155/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9953 - rec_loss: 0.4941 - kl_loss: 0.5012\n",
      "Epoch 156/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9941 - rec_loss: 0.4904 - kl_loss: 0.5037\n",
      "Epoch 157/1000\n",
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      "Epoch 158/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9900 - rec_loss: 0.4857 - kl_loss: 0.5043\n",
      "Epoch 159/1000\n",
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      "Epoch 160/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9854 - rec_loss: 0.4811 - kl_loss: 0.5043\n",
      "Epoch 161/1000\n",
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      "Epoch 162/1000\n",
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      "Epoch 163/1000\n",
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      "Epoch 164/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9758 - rec_loss: 0.4714 - kl_loss: 0.5044\n",
      "Epoch 165/1000\n",
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      "Epoch 166/1000\n",
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      "Epoch 167/1000\n",
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      "Epoch 168/1000\n",
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      "Epoch 169/1000\n",
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      "Epoch 170/1000\n",
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      "Epoch 171/1000\n",
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      "Epoch 172/1000\n",
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      "Epoch 173/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9565 - rec_loss: 0.4523 - kl_loss: 0.5042\n",
      "Epoch 174/1000\n",
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      "Epoch 175/1000\n",
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      "Epoch 176/1000\n",
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      "Epoch 177/1000\n",
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      "Epoch 178/1000\n",
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      "Epoch 179/1000\n",
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      "Epoch 180/1000\n",
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      "Epoch 181/1000\n",
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      "Epoch 182/1000\n",
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      "Epoch 183/1000\n",
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      "Epoch 184/1000\n",
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      "Epoch 185/1000\n",
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      "Epoch 186/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.9240 - rec_loss: 0.4171 - kl_loss: 0.5069\n",
      "Epoch 187/1000\n",
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      "Epoch 188/1000\n",
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      "Epoch 189/1000\n",
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      "Epoch 190/1000\n",
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      "Epoch 191/1000\n",
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      "Epoch 192/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9083 - rec_loss: 0.4021 - kl_loss: 0.5062\n",
      "Epoch 193/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9081 - rec_loss: 0.4008 - kl_loss: 0.5072\n",
      "Epoch 194/1000\n",
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      "Epoch 195/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.9032 - rec_loss: 0.3968 - kl_loss: 0.5064\n",
      "Epoch 196/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8988 - rec_loss: 0.3925 - kl_loss: 0.5062\n",
      "Epoch 197/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8977 - rec_loss: 0.3930 - kl_loss: 0.5047\n",
      "Epoch 198/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8965 - rec_loss: 0.3897 - kl_loss: 0.5069\n",
      "Epoch 199/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8951 - rec_loss: 0.3888 - kl_loss: 0.5063\n",
      "Epoch 200/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8939 - rec_loss: 0.3869 - kl_loss: 0.5070\n",
      "Epoch 201/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.8929 - rec_loss: 0.3857 - kl_loss: 0.5072\n",
      "Epoch 202/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.8879 - rec_loss: 0.3816 - kl_loss: 0.5063\n",
      "Epoch 203/1000\n",
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      "Epoch 204/1000\n",
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      "Epoch 229/1000\n",
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      "Epoch 230/1000\n",
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      "Epoch 231/1000\n",
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      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 235/1000\n",
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      "Epoch 239/1000\n",
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      "Epoch 244/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7967 - rec_loss: 0.2885 - kl_loss: 0.5083\n",
      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7955 - rec_loss: 0.2877 - kl_loss: 0.5079\n",
      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7912 - rec_loss: 0.2834 - kl_loss: 0.5078\n",
      "Epoch 261/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7903 - rec_loss: 0.2820 - kl_loss: 0.5083\n",
      "Epoch 262/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7897 - rec_loss: 0.2827 - kl_loss: 0.5070\n",
      "Epoch 263/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7900 - rec_loss: 0.2809 - kl_loss: 0.5091\n",
      "Epoch 264/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7880 - rec_loss: 0.2791 - kl_loss: 0.5089\n",
      "Epoch 265/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7838 - rec_loss: 0.2765 - kl_loss: 0.5073\n",
      "Epoch 266/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7803 - rec_loss: 0.2734 - kl_loss: 0.5069\n",
      "Epoch 267/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7825 - rec_loss: 0.2742 - kl_loss: 0.5083\n",
      "Epoch 268/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7807 - rec_loss: 0.2726 - kl_loss: 0.5082\n",
      "Epoch 269/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7772 - rec_loss: 0.2698 - kl_loss: 0.5073\n",
      "Epoch 270/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7776 - rec_loss: 0.2697 - kl_loss: 0.5079\n",
      "Epoch 271/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.7764 - rec_loss: 0.2678 - kl_loss: 0.5086\n",
      "Epoch 272/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7733 - rec_loss: 0.2659 - kl_loss: 0.5073\n",
      "Epoch 273/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7721 - rec_loss: 0.2649 - kl_loss: 0.5072\n",
      "Epoch 274/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7716 - rec_loss: 0.2630 - kl_loss: 0.5086\n",
      "Epoch 275/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7704 - rec_loss: 0.2627 - kl_loss: 0.5077\n",
      "Epoch 276/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7693 - rec_loss: 0.2608 - kl_loss: 0.5085\n",
      "Epoch 277/1000\n",
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      "Epoch 278/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7669 - rec_loss: 0.2577 - kl_loss: 0.5092\n",
      "Epoch 279/1000\n",
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      "Epoch 280/1000\n",
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      "Epoch 281/1000\n",
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      "Epoch 282/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7615 - rec_loss: 0.2525 - kl_loss: 0.5089\n",
      "Epoch 283/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7584 - rec_loss: 0.2502 - kl_loss: 0.5082\n",
      "Epoch 284/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7569 - rec_loss: 0.2495 - kl_loss: 0.5075\n",
      "Epoch 285/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7592 - rec_loss: 0.2509 - kl_loss: 0.5083\n",
      "Epoch 286/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7544 - rec_loss: 0.2457 - kl_loss: 0.5087\n",
      "Epoch 287/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7534 - rec_loss: 0.2450 - kl_loss: 0.5083\n",
      "Epoch 288/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7523 - rec_loss: 0.2434 - kl_loss: 0.5089\n",
      "Epoch 289/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7515 - rec_loss: 0.2433 - kl_loss: 0.5082\n",
      "Epoch 290/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.7480 - rec_loss: 0.2399 - kl_loss: 0.5082\n",
      "Epoch 291/1000\n",
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      "Epoch 292/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7462 - rec_loss: 0.2389 - kl_loss: 0.5073\n",
      "Epoch 293/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7454 - rec_loss: 0.2370 - kl_loss: 0.5084\n",
      "Epoch 294/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7442 - rec_loss: 0.2355 - kl_loss: 0.5088\n",
      "Epoch 295/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7413 - rec_loss: 0.2346 - kl_loss: 0.5067\n",
      "Epoch 296/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7400 - rec_loss: 0.2326 - kl_loss: 0.5074\n",
      "Epoch 297/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7401 - rec_loss: 0.2319 - kl_loss: 0.5081\n",
      "Epoch 298/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7395 - rec_loss: 0.2314 - kl_loss: 0.5081\n",
      "Epoch 299/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7366 - rec_loss: 0.2293 - kl_loss: 0.5073\n",
      "Epoch 300/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7384 - rec_loss: 0.2299 - kl_loss: 0.5085\n",
      "Epoch 301/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7371 - rec_loss: 0.2288 - kl_loss: 0.5083\n",
      "Epoch 302/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7362 - rec_loss: 0.2279 - kl_loss: 0.5082\n",
      "Epoch 303/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7362 - rec_loss: 0.2275 - kl_loss: 0.5087\n",
      "Epoch 304/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7341 - rec_loss: 0.2269 - kl_loss: 0.5072\n",
      "Epoch 305/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7317 - rec_loss: 0.2240 - kl_loss: 0.5078\n",
      "Epoch 306/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7317 - rec_loss: 0.2220 - kl_loss: 0.5097\n",
      "Epoch 307/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7312 - rec_loss: 0.2228 - kl_loss: 0.5084\n",
      "Epoch 308/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7296 - rec_loss: 0.2203 - kl_loss: 0.5093\n",
      "Epoch 309/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7267 - rec_loss: 0.2186 - kl_loss: 0.5081\n",
      "Epoch 310/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7257 - rec_loss: 0.2185 - kl_loss: 0.5072\n",
      "Epoch 311/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7244 - rec_loss: 0.2177 - kl_loss: 0.5067\n",
      "Epoch 312/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7279 - rec_loss: 0.2190 - kl_loss: 0.5089\n",
      "Epoch 313/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7229 - rec_loss: 0.2156 - kl_loss: 0.5073\n",
      "Epoch 314/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7207 - rec_loss: 0.2127 - kl_loss: 0.5080\n",
      "Epoch 315/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.7207 - rec_loss: 0.2136 - kl_loss: 0.5071\n",
      "Epoch 316/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7210 - rec_loss: 0.2142 - kl_loss: 0.5068\n",
      "Epoch 317/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.7190 - rec_loss: 0.2119 - kl_loss: 0.5071\n",
      "Epoch 318/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7204 - rec_loss: 0.2123 - kl_loss: 0.5081\n",
      "Epoch 319/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7192 - rec_loss: 0.2109 - kl_loss: 0.5083\n",
      "Epoch 320/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7168 - rec_loss: 0.2091 - kl_loss: 0.5076\n",
      "Epoch 321/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7146 - rec_loss: 0.2077 - kl_loss: 0.5069\n",
      "Epoch 322/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7137 - rec_loss: 0.2078 - kl_loss: 0.5059\n",
      "Epoch 323/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7125 - rec_loss: 0.2067 - kl_loss: 0.5057\n",
      "Epoch 324/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7114 - rec_loss: 0.2048 - kl_loss: 0.5066\n",
      "Epoch 325/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7119 - rec_loss: 0.2046 - kl_loss: 0.5073\n",
      "Epoch 326/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.7128 - rec_loss: 0.2057 - kl_loss: 0.5071\n",
      "Epoch 327/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7098 - rec_loss: 0.2021 - kl_loss: 0.5077\n",
      "Epoch 328/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7096 - rec_loss: 0.2026 - kl_loss: 0.5070\n",
      "Epoch 329/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7120 - rec_loss: 0.2059 - kl_loss: 0.5062\n",
      "Epoch 330/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7085 - rec_loss: 0.2014 - kl_loss: 0.5071\n",
      "Epoch 331/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7064 - rec_loss: 0.2003 - kl_loss: 0.5061\n",
      "Epoch 332/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7038 - rec_loss: 0.1977 - kl_loss: 0.5061\n",
      "Epoch 333/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7037 - rec_loss: 0.1986 - kl_loss: 0.5051\n",
      "Epoch 334/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7048 - rec_loss: 0.1977 - kl_loss: 0.5071\n",
      "Epoch 335/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.7025 - rec_loss: 0.1960 - kl_loss: 0.5065\n",
      "Epoch 336/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.7040 - rec_loss: 0.1966 - kl_loss: 0.5074\n",
      "Epoch 337/1000\n",
      "97/97 [==============================] - 2s 23ms/step - tot_loss: 0.7044 - rec_loss: 0.1967 - kl_loss: 0.5077\n",
      "Epoch 338/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.7009 - rec_loss: 0.1954 - kl_loss: 0.5055\n",
      "Epoch 339/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6994 - rec_loss: 0.1940 - kl_loss: 0.5053\n",
      "Epoch 340/1000\n",
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      "Epoch 341/1000\n",
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      "Epoch 348/1000\n",
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      "Epoch 350/1000\n",
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      "Epoch 351/1000\n",
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      "Epoch 356/1000\n",
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      "Epoch 357/1000\n",
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      "Epoch 358/1000\n",
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      "Epoch 359/1000\n",
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      "Epoch 360/1000\n",
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      "Epoch 361/1000\n",
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      "Epoch 362/1000\n",
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      "Epoch 363/1000\n",
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      "Epoch 365/1000\n",
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      "Epoch 367/1000\n",
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      "Epoch 368/1000\n",
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      "Epoch 369/1000\n",
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      "Epoch 371/1000\n",
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      "Epoch 373/1000\n",
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      "Epoch 374/1000\n",
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      "Epoch 375/1000\n",
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      "Epoch 376/1000\n",
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      "Epoch 377/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6724 - rec_loss: 0.1694 - kl_loss: 0.5031\n",
      "Epoch 378/1000\n",
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      "Epoch 379/1000\n",
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      "Epoch 380/1000\n",
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      "Epoch 381/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6736 - rec_loss: 0.1679 - kl_loss: 0.5057\n",
      "Epoch 382/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6695 - rec_loss: 0.1670 - kl_loss: 0.5024\n",
      "Epoch 383/1000\n",
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      "Epoch 384/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6703 - rec_loss: 0.1661 - kl_loss: 0.5042\n",
      "Epoch 385/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6679 - rec_loss: 0.1650 - kl_loss: 0.5029\n",
      "Epoch 386/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6659 - rec_loss: 0.1635 - kl_loss: 0.5024\n",
      "Epoch 387/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6659 - rec_loss: 0.1636 - kl_loss: 0.5023\n",
      "Epoch 388/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6672 - rec_loss: 0.1647 - kl_loss: 0.5025\n",
      "Epoch 389/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6662 - rec_loss: 0.1630 - kl_loss: 0.5032\n",
      "Epoch 390/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6674 - rec_loss: 0.1638 - kl_loss: 0.5037\n",
      "Epoch 391/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6645 - rec_loss: 0.1623 - kl_loss: 0.5022\n",
      "Epoch 392/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6627 - rec_loss: 0.1621 - kl_loss: 0.5006\n",
      "Epoch 393/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6635 - rec_loss: 0.1610 - kl_loss: 0.5025\n",
      "Epoch 394/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6627 - rec_loss: 0.1608 - kl_loss: 0.5019\n",
      "Epoch 395/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6603 - rec_loss: 0.1599 - kl_loss: 0.5003\n",
      "Epoch 396/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6625 - rec_loss: 0.1603 - kl_loss: 0.5021\n",
      "Epoch 397/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6634 - rec_loss: 0.1592 - kl_loss: 0.5041\n",
      "Epoch 398/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6612 - rec_loss: 0.1583 - kl_loss: 0.5029\n",
      "Epoch 399/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6633 - rec_loss: 0.1588 - kl_loss: 0.5045\n",
      "Epoch 400/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6611 - rec_loss: 0.1576 - kl_loss: 0.5035\n",
      "Epoch 401/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6600 - rec_loss: 0.1573 - kl_loss: 0.5028\n",
      "Epoch 402/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6611 - rec_loss: 0.1580 - kl_loss: 0.5031\n",
      "Epoch 403/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6597 - rec_loss: 0.1576 - kl_loss: 0.5021\n",
      "Epoch 404/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6589 - rec_loss: 0.1568 - kl_loss: 0.5020\n",
      "Epoch 405/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6585 - rec_loss: 0.1557 - kl_loss: 0.5028\n",
      "Epoch 406/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6604 - rec_loss: 0.1571 - kl_loss: 0.5033\n",
      "Epoch 407/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6578 - rec_loss: 0.1556 - kl_loss: 0.5023\n",
      "Epoch 408/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6571 - rec_loss: 0.1562 - kl_loss: 0.5009\n",
      "Epoch 409/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6572 - rec_loss: 0.1549 - kl_loss: 0.5023\n",
      "Epoch 410/1000\n",
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      "Epoch 411/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6551 - rec_loss: 0.1534 - kl_loss: 0.5017\n",
      "Epoch 412/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6564 - rec_loss: 0.1544 - kl_loss: 0.5020\n",
      "Epoch 413/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6524 - rec_loss: 0.1517 - kl_loss: 0.5006\n",
      "Epoch 414/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6552 - rec_loss: 0.1527 - kl_loss: 0.5024\n",
      "Epoch 415/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6525 - rec_loss: 0.1511 - kl_loss: 0.5014\n",
      "Epoch 416/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6524 - rec_loss: 0.1510 - kl_loss: 0.5013\n",
      "Epoch 417/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6535 - rec_loss: 0.1506 - kl_loss: 0.5029\n",
      "Epoch 418/1000\n",
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      "Epoch 419/1000\n",
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      "Epoch 420/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6528 - rec_loss: 0.1509 - kl_loss: 0.5019\n",
      "Epoch 421/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6524 - rec_loss: 0.1497 - kl_loss: 0.5028\n",
      "Epoch 422/1000\n",
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      "Epoch 423/1000\n",
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      "Epoch 424/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6495 - rec_loss: 0.1488 - kl_loss: 0.5007\n",
      "Epoch 425/1000\n",
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      "Epoch 426/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6467 - rec_loss: 0.1471 - kl_loss: 0.4995\n",
      "Epoch 427/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6477 - rec_loss: 0.1458 - kl_loss: 0.5019\n",
      "Epoch 428/1000\n",
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      "Epoch 429/1000\n",
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      "Epoch 430/1000\n",
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      "Epoch 431/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6465 - rec_loss: 0.1452 - kl_loss: 0.5013\n",
      "Epoch 432/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6464 - rec_loss: 0.1458 - kl_loss: 0.5007\n",
      "Epoch 433/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6447 - rec_loss: 0.1438 - kl_loss: 0.5009\n",
      "Epoch 434/1000\n",
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      "Epoch 435/1000\n",
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      "Epoch 436/1000\n",
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      "Epoch 437/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6434 - rec_loss: 0.1436 - kl_loss: 0.4997\n",
      "Epoch 438/1000\n",
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      "Epoch 439/1000\n",
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      "Epoch 440/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6430 - rec_loss: 0.1431 - kl_loss: 0.4999\n",
      "Epoch 441/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6421 - rec_loss: 0.1406 - kl_loss: 0.5015\n",
      "Epoch 442/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6429 - rec_loss: 0.1434 - kl_loss: 0.4995\n",
      "Epoch 443/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6422 - rec_loss: 0.1419 - kl_loss: 0.5003\n",
      "Epoch 444/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6416 - rec_loss: 0.1413 - kl_loss: 0.5004\n",
      "Epoch 445/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6417 - rec_loss: 0.1410 - kl_loss: 0.5007\n",
      "Epoch 446/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6402 - rec_loss: 0.1402 - kl_loss: 0.5000\n",
      "Epoch 447/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6416 - rec_loss: 0.1416 - kl_loss: 0.5000\n",
      "Epoch 448/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6399 - rec_loss: 0.1396 - kl_loss: 0.5003\n",
      "Epoch 449/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6406 - rec_loss: 0.1403 - kl_loss: 0.5003\n",
      "Epoch 450/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6389 - rec_loss: 0.1392 - kl_loss: 0.4997\n",
      "Epoch 451/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6413 - rec_loss: 0.1397 - kl_loss: 0.5016\n",
      "Epoch 452/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6401 - rec_loss: 0.1396 - kl_loss: 0.5005\n",
      "Epoch 453/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6376 - rec_loss: 0.1382 - kl_loss: 0.4994\n",
      "Epoch 454/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6386 - rec_loss: 0.1387 - kl_loss: 0.4999\n",
      "Epoch 455/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6386 - rec_loss: 0.1380 - kl_loss: 0.5006\n",
      "Epoch 456/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6396 - rec_loss: 0.1380 - kl_loss: 0.5017\n",
      "Epoch 457/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6388 - rec_loss: 0.1389 - kl_loss: 0.4998\n",
      "Epoch 458/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6363 - rec_loss: 0.1369 - kl_loss: 0.4994\n",
      "Epoch 459/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6378 - rec_loss: 0.1363 - kl_loss: 0.5014\n",
      "Epoch 460/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6361 - rec_loss: 0.1363 - kl_loss: 0.4998\n",
      "Epoch 461/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6367 - rec_loss: 0.1364 - kl_loss: 0.5004\n",
      "Epoch 462/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6341 - rec_loss: 0.1345 - kl_loss: 0.4996\n",
      "Epoch 463/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6335 - rec_loss: 0.1351 - kl_loss: 0.4984\n",
      "Epoch 464/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6351 - rec_loss: 0.1347 - kl_loss: 0.5004\n",
      "Epoch 465/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6348 - rec_loss: 0.1350 - kl_loss: 0.4997\n",
      "Epoch 466/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6348 - rec_loss: 0.1350 - kl_loss: 0.4998\n",
      "Epoch 467/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6343 - rec_loss: 0.1353 - kl_loss: 0.4990\n",
      "Epoch 468/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6330 - rec_loss: 0.1341 - kl_loss: 0.4989\n",
      "Epoch 469/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6327 - rec_loss: 0.1341 - kl_loss: 0.4986\n",
      "Epoch 470/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6346 - rec_loss: 0.1341 - kl_loss: 0.5005\n",
      "Epoch 471/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6332 - rec_loss: 0.1333 - kl_loss: 0.4999\n",
      "Epoch 472/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6332 - rec_loss: 0.1343 - kl_loss: 0.4989\n",
      "Epoch 473/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6320 - rec_loss: 0.1322 - kl_loss: 0.4998\n",
      "Epoch 474/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6338 - rec_loss: 0.1343 - kl_loss: 0.4994\n",
      "Epoch 475/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6294 - rec_loss: 0.1308 - kl_loss: 0.4986\n",
      "Epoch 476/1000\n",
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      "Epoch 478/1000\n",
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      "Epoch 480/1000\n",
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      "Epoch 481/1000\n",
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      "Epoch 482/1000\n",
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      "Epoch 483/1000\n",
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      "Epoch 487/1000\n",
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      "Epoch 489/1000\n",
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      "Epoch 490/1000\n",
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      "Epoch 491/1000\n",
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      "Epoch 492/1000\n",
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      "Epoch 493/1000\n",
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      "Epoch 494/1000\n",
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      "Epoch 495/1000\n",
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      "Epoch 496/1000\n",
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      "Epoch 497/1000\n",
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      "Epoch 498/1000\n",
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      "Epoch 499/1000\n",
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      "Epoch 500/1000\n",
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      "Epoch 501/1000\n",
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      "Epoch 502/1000\n",
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      "Epoch 503/1000\n",
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      "Epoch 504/1000\n",
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      "Epoch 505/1000\n",
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      "Epoch 506/1000\n",
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      "Epoch 507/1000\n",
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      "Epoch 508/1000\n",
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      "Epoch 509/1000\n",
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      "Epoch 510/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6221 - rec_loss: 0.1252 - kl_loss: 0.4969\n",
      "Epoch 511/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6226 - rec_loss: 0.1245 - kl_loss: 0.4981\n",
      "Epoch 512/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6217 - rec_loss: 0.1232 - kl_loss: 0.4985\n",
      "Epoch 513/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6220 - rec_loss: 0.1236 - kl_loss: 0.4984\n",
      "Epoch 514/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6206 - rec_loss: 0.1236 - kl_loss: 0.4970\n",
      "Epoch 515/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6211 - rec_loss: 0.1234 - kl_loss: 0.4977\n",
      "Epoch 516/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6198 - rec_loss: 0.1232 - kl_loss: 0.4966\n",
      "Epoch 517/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6200 - rec_loss: 0.1230 - kl_loss: 0.4970\n",
      "Epoch 518/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6220 - rec_loss: 0.1234 - kl_loss: 0.4985\n",
      "Epoch 519/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6198 - rec_loss: 0.1229 - kl_loss: 0.4968\n",
      "Epoch 520/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6170 - rec_loss: 0.1211 - kl_loss: 0.4959\n",
      "Epoch 521/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6196 - rec_loss: 0.1222 - kl_loss: 0.4974\n",
      "Epoch 522/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6201 - rec_loss: 0.1224 - kl_loss: 0.4977\n",
      "Epoch 523/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6175 - rec_loss: 0.1215 - kl_loss: 0.4960\n",
      "Epoch 524/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6195 - rec_loss: 0.1223 - kl_loss: 0.4971\n",
      "Epoch 525/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6191 - rec_loss: 0.1218 - kl_loss: 0.4973\n",
      "Epoch 526/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6170 - rec_loss: 0.1199 - kl_loss: 0.4970\n",
      "Epoch 527/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6183 - rec_loss: 0.1213 - kl_loss: 0.4970\n",
      "Epoch 528/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6180 - rec_loss: 0.1212 - kl_loss: 0.4968\n",
      "Epoch 529/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6180 - rec_loss: 0.1216 - kl_loss: 0.4965\n",
      "Epoch 530/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6170 - rec_loss: 0.1209 - kl_loss: 0.4962\n",
      "Epoch 531/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6172 - rec_loss: 0.1198 - kl_loss: 0.4973\n",
      "Epoch 532/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6182 - rec_loss: 0.1202 - kl_loss: 0.4981\n",
      "Epoch 533/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6189 - rec_loss: 0.1206 - kl_loss: 0.4984\n",
      "Epoch 534/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6172 - rec_loss: 0.1202 - kl_loss: 0.4970\n",
      "Epoch 535/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6184 - rec_loss: 0.1212 - kl_loss: 0.4972\n",
      "Epoch 536/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6160 - rec_loss: 0.1187 - kl_loss: 0.4972\n",
      "Epoch 537/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6145 - rec_loss: 0.1183 - kl_loss: 0.4961\n",
      "Epoch 538/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6147 - rec_loss: 0.1180 - kl_loss: 0.4967\n",
      "Epoch 539/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6146 - rec_loss: 0.1184 - kl_loss: 0.4962\n",
      "Epoch 540/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6150 - rec_loss: 0.1183 - kl_loss: 0.4968\n",
      "Epoch 541/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6147 - rec_loss: 0.1181 - kl_loss: 0.4966\n",
      "Epoch 542/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6151 - rec_loss: 0.1186 - kl_loss: 0.4965\n",
      "Epoch 543/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6154 - rec_loss: 0.1185 - kl_loss: 0.4969\n",
      "Epoch 544/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6174 - rec_loss: 0.1191 - kl_loss: 0.4983\n",
      "Epoch 545/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6163 - rec_loss: 0.1181 - kl_loss: 0.4982\n",
      "Epoch 546/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6132 - rec_loss: 0.1163 - kl_loss: 0.4969\n",
      "Epoch 547/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6152 - rec_loss: 0.1180 - kl_loss: 0.4972\n",
      "Epoch 548/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6133 - rec_loss: 0.1170 - kl_loss: 0.4963\n",
      "Epoch 549/1000\n",
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      "Epoch 550/1000\n",
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      "Epoch 551/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6126 - rec_loss: 0.1160 - kl_loss: 0.4967\n",
      "Epoch 552/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6123 - rec_loss: 0.1167 - kl_loss: 0.4956\n",
      "Epoch 553/1000\n",
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      "Epoch 554/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6129 - rec_loss: 0.1163 - kl_loss: 0.4966\n",
      "Epoch 555/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6137 - rec_loss: 0.1164 - kl_loss: 0.4973\n",
      "Epoch 556/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6118 - rec_loss: 0.1157 - kl_loss: 0.4961\n",
      "Epoch 557/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6113 - rec_loss: 0.1158 - kl_loss: 0.4955\n",
      "Epoch 558/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6131 - rec_loss: 0.1159 - kl_loss: 0.4972\n",
      "Epoch 559/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6107 - rec_loss: 0.1141 - kl_loss: 0.4965\n",
      "Epoch 560/1000\n",
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      "Epoch 561/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6158 - rec_loss: 0.1165 - kl_loss: 0.4993\n",
      "Epoch 562/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6110 - rec_loss: 0.1147 - kl_loss: 0.4963\n",
      "Epoch 563/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6110 - rec_loss: 0.1148 - kl_loss: 0.4963\n",
      "Epoch 564/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6100 - rec_loss: 0.1143 - kl_loss: 0.4957\n",
      "Epoch 565/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6134 - rec_loss: 0.1155 - kl_loss: 0.4979\n",
      "Epoch 566/1000\n",
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      "Epoch 567/1000\n",
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      "Epoch 568/1000\n",
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      "Epoch 569/1000\n",
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      "Epoch 570/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6096 - rec_loss: 0.1131 - kl_loss: 0.4965\n",
      "Epoch 571/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6088 - rec_loss: 0.1132 - kl_loss: 0.4956\n",
      "Epoch 572/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6096 - rec_loss: 0.1141 - kl_loss: 0.4955\n",
      "Epoch 573/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6087 - rec_loss: 0.1130 - kl_loss: 0.4957\n",
      "Epoch 574/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6093 - rec_loss: 0.1139 - kl_loss: 0.4955\n",
      "Epoch 575/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6082 - rec_loss: 0.1122 - kl_loss: 0.4961\n",
      "Epoch 576/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6070 - rec_loss: 0.1122 - kl_loss: 0.4948\n",
      "Epoch 577/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6085 - rec_loss: 0.1130 - kl_loss: 0.4955\n",
      "Epoch 578/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6064 - rec_loss: 0.1122 - kl_loss: 0.4942\n",
      "Epoch 579/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6056 - rec_loss: 0.1117 - kl_loss: 0.4938\n",
      "Epoch 580/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6072 - rec_loss: 0.1128 - kl_loss: 0.4944\n",
      "Epoch 581/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6073 - rec_loss: 0.1123 - kl_loss: 0.4950\n",
      "Epoch 582/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6077 - rec_loss: 0.1122 - kl_loss: 0.4955\n",
      "Epoch 583/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6055 - rec_loss: 0.1116 - kl_loss: 0.4939\n",
      "Epoch 584/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6062 - rec_loss: 0.1109 - kl_loss: 0.4953\n",
      "Epoch 585/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6069 - rec_loss: 0.1105 - kl_loss: 0.4964\n",
      "Epoch 586/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6076 - rec_loss: 0.1115 - kl_loss: 0.4962\n",
      "Epoch 587/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6070 - rec_loss: 0.1115 - kl_loss: 0.4955\n",
      "Epoch 588/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6068 - rec_loss: 0.1108 - kl_loss: 0.4960\n",
      "Epoch 589/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6069 - rec_loss: 0.1122 - kl_loss: 0.4947\n",
      "Epoch 590/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6057 - rec_loss: 0.1110 - kl_loss: 0.4947\n",
      "Epoch 591/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6071 - rec_loss: 0.1115 - kl_loss: 0.4956\n",
      "Epoch 592/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6059 - rec_loss: 0.1112 - kl_loss: 0.4947\n",
      "Epoch 593/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6058 - rec_loss: 0.1109 - kl_loss: 0.4949\n",
      "Epoch 594/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6061 - rec_loss: 0.1105 - kl_loss: 0.4956\n",
      "Epoch 595/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6035 - rec_loss: 0.1097 - kl_loss: 0.4938\n",
      "Epoch 596/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6046 - rec_loss: 0.1090 - kl_loss: 0.4956\n",
      "Epoch 597/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6051 - rec_loss: 0.1093 - kl_loss: 0.4958\n",
      "Epoch 598/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6046 - rec_loss: 0.1105 - kl_loss: 0.4941\n",
      "Epoch 599/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6046 - rec_loss: 0.1093 - kl_loss: 0.4953\n",
      "Epoch 600/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6036 - rec_loss: 0.1094 - kl_loss: 0.4942\n",
      "Epoch 601/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6041 - rec_loss: 0.1084 - kl_loss: 0.4957\n",
      "Epoch 602/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6022 - rec_loss: 0.1083 - kl_loss: 0.4938\n",
      "Epoch 603/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6029 - rec_loss: 0.1094 - kl_loss: 0.4935\n",
      "Epoch 604/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6009 - rec_loss: 0.1076 - kl_loss: 0.4933\n",
      "Epoch 605/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6037 - rec_loss: 0.1082 - kl_loss: 0.4955\n",
      "Epoch 606/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6024 - rec_loss: 0.1075 - kl_loss: 0.4949\n",
      "Epoch 607/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.6016 - rec_loss: 0.1081 - kl_loss: 0.4935\n",
      "Epoch 608/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6033 - rec_loss: 0.1088 - kl_loss: 0.4945\n",
      "Epoch 609/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6053 - rec_loss: 0.1097 - kl_loss: 0.4956\n",
      "Epoch 610/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.6039 - rec_loss: 0.1091 - kl_loss: 0.4947\n",
      "Epoch 611/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.6041 - rec_loss: 0.1086 - kl_loss: 0.4955\n",
      "Epoch 612/1000\n",
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      "Epoch 613/1000\n",
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      "Epoch 614/1000\n",
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      "Epoch 615/1000\n",
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      "Epoch 617/1000\n",
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      "Epoch 618/1000\n",
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      "Epoch 619/1000\n",
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      "Epoch 620/1000\n",
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      "Epoch 621/1000\n",
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      "Epoch 622/1000\n",
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      "Epoch 623/1000\n",
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      "Epoch 624/1000\n",
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      "Epoch 625/1000\n",
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      "Epoch 626/1000\n",
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      "Epoch 627/1000\n",
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      "Epoch 628/1000\n",
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      "Epoch 629/1000\n",
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      "Epoch 630/1000\n",
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      "Epoch 631/1000\n",
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      "Epoch 632/1000\n",
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      "Epoch 633/1000\n",
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      "Epoch 634/1000\n",
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      "Epoch 635/1000\n",
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      "Epoch 636/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5968 - rec_loss: 0.1038 - kl_loss: 0.4930\n",
      "Epoch 637/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5966 - rec_loss: 0.1041 - kl_loss: 0.4925\n",
      "Epoch 638/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5970 - rec_loss: 0.1045 - kl_loss: 0.4924\n",
      "Epoch 639/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5975 - rec_loss: 0.1038 - kl_loss: 0.4937\n",
      "Epoch 640/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5962 - rec_loss: 0.1033 - kl_loss: 0.4929\n",
      "Epoch 641/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5969 - rec_loss: 0.1053 - kl_loss: 0.4916\n",
      "Epoch 642/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5997 - rec_loss: 0.1063 - kl_loss: 0.4934\n",
      "Epoch 643/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5960 - rec_loss: 0.1036 - kl_loss: 0.4924\n",
      "Epoch 644/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5971 - rec_loss: 0.1048 - kl_loss: 0.4923\n",
      "Epoch 645/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5957 - rec_loss: 0.1031 - kl_loss: 0.4926\n",
      "Epoch 646/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5978 - rec_loss: 0.1048 - kl_loss: 0.4930\n",
      "Epoch 647/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5951 - rec_loss: 0.1030 - kl_loss: 0.4920\n",
      "Epoch 648/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5960 - rec_loss: 0.1030 - kl_loss: 0.4929\n",
      "Epoch 649/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5950 - rec_loss: 0.1028 - kl_loss: 0.4922\n",
      "Epoch 650/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5971 - rec_loss: 0.1037 - kl_loss: 0.4933\n",
      "Epoch 651/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5959 - rec_loss: 0.1036 - kl_loss: 0.4923\n",
      "Epoch 652/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5952 - rec_loss: 0.1022 - kl_loss: 0.4930\n",
      "Epoch 653/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5955 - rec_loss: 0.1037 - kl_loss: 0.4918\n",
      "Epoch 654/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5984 - rec_loss: 0.1046 - kl_loss: 0.4938\n",
      "Epoch 655/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5962 - rec_loss: 0.1032 - kl_loss: 0.4930\n",
      "Epoch 656/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5952 - rec_loss: 0.1018 - kl_loss: 0.4935\n",
      "Epoch 657/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5942 - rec_loss: 0.1018 - kl_loss: 0.4924\n",
      "Epoch 658/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5943 - rec_loss: 0.1025 - kl_loss: 0.4918\n",
      "Epoch 659/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5956 - rec_loss: 0.1020 - kl_loss: 0.4936\n",
      "Epoch 660/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5927 - rec_loss: 0.1015 - kl_loss: 0.4912\n",
      "Epoch 661/1000\n",
      "97/97 [==============================] - 2s 23ms/step - tot_loss: 0.5961 - rec_loss: 0.1023 - kl_loss: 0.4938\n",
      "Epoch 662/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5942 - rec_loss: 0.1023 - kl_loss: 0.4919\n",
      "Epoch 663/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5936 - rec_loss: 0.1021 - kl_loss: 0.4915\n",
      "Epoch 664/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5929 - rec_loss: 0.1012 - kl_loss: 0.4916\n",
      "Epoch 665/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5955 - rec_loss: 0.1025 - kl_loss: 0.4929\n",
      "Epoch 666/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5945 - rec_loss: 0.1021 - kl_loss: 0.4924\n",
      "Epoch 667/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5946 - rec_loss: 0.1022 - kl_loss: 0.4923\n",
      "Epoch 668/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5927 - rec_loss: 0.1009 - kl_loss: 0.4918\n",
      "Epoch 669/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5948 - rec_loss: 0.1022 - kl_loss: 0.4926\n",
      "Epoch 670/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5932 - rec_loss: 0.1019 - kl_loss: 0.4914\n",
      "Epoch 671/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5949 - rec_loss: 0.1020 - kl_loss: 0.4929\n",
      "Epoch 672/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5957 - rec_loss: 0.1028 - kl_loss: 0.4929\n",
      "Epoch 673/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5992 - rec_loss: 0.1031 - kl_loss: 0.4961\n",
      "Epoch 674/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5950 - rec_loss: 0.1018 - kl_loss: 0.4932\n",
      "Epoch 675/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5907 - rec_loss: 0.0997 - kl_loss: 0.4910\n",
      "Epoch 676/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5932 - rec_loss: 0.1013 - kl_loss: 0.4920\n",
      "Epoch 677/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5922 - rec_loss: 0.1003 - kl_loss: 0.4918\n",
      "Epoch 678/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5947 - rec_loss: 0.1014 - kl_loss: 0.4933\n",
      "Epoch 679/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5928 - rec_loss: 0.1009 - kl_loss: 0.4919\n",
      "Epoch 680/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5923 - rec_loss: 0.0999 - kl_loss: 0.4924\n",
      "Epoch 681/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5913 - rec_loss: 0.1003 - kl_loss: 0.4910\n",
      "Epoch 682/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5910 - rec_loss: 0.0998 - kl_loss: 0.4911\n",
      "Epoch 683/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5903 - rec_loss: 0.0996 - kl_loss: 0.4907\n",
      "Epoch 684/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5903 - rec_loss: 0.0999 - kl_loss: 0.4904\n",
      "Epoch 685/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5922 - rec_loss: 0.0999 - kl_loss: 0.4923\n",
      "Epoch 686/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5906 - rec_loss: 0.0984 - kl_loss: 0.4922\n",
      "Epoch 687/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5903 - rec_loss: 0.0991 - kl_loss: 0.4912\n",
      "Epoch 688/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5902 - rec_loss: 0.0996 - kl_loss: 0.4906\n",
      "Epoch 689/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5896 - rec_loss: 0.0994 - kl_loss: 0.4903\n",
      "Epoch 690/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5913 - rec_loss: 0.0997 - kl_loss: 0.4916\n",
      "Epoch 691/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5908 - rec_loss: 0.0991 - kl_loss: 0.4917\n",
      "Epoch 692/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5917 - rec_loss: 0.0995 - kl_loss: 0.4923\n",
      "Epoch 693/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5904 - rec_loss: 0.0982 - kl_loss: 0.4921\n",
      "Epoch 694/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5914 - rec_loss: 0.0994 - kl_loss: 0.4920\n",
      "Epoch 695/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5890 - rec_loss: 0.0987 - kl_loss: 0.4903\n",
      "Epoch 696/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5899 - rec_loss: 0.0989 - kl_loss: 0.4910\n",
      "Epoch 697/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5905 - rec_loss: 0.0994 - kl_loss: 0.4911\n",
      "Epoch 698/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5899 - rec_loss: 0.0994 - kl_loss: 0.4905\n",
      "Epoch 699/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5911 - rec_loss: 0.0995 - kl_loss: 0.4916\n",
      "Epoch 700/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5890 - rec_loss: 0.0970 - kl_loss: 0.4920\n",
      "Epoch 701/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5908 - rec_loss: 0.0988 - kl_loss: 0.4920\n",
      "Epoch 702/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5882 - rec_loss: 0.0977 - kl_loss: 0.4905\n",
      "Epoch 703/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5907 - rec_loss: 0.0988 - kl_loss: 0.4920\n",
      "Epoch 704/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5888 - rec_loss: 0.0972 - kl_loss: 0.4916\n",
      "Epoch 705/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5865 - rec_loss: 0.0969 - kl_loss: 0.4896\n",
      "Epoch 706/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5889 - rec_loss: 0.0976 - kl_loss: 0.4914\n",
      "Epoch 707/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5881 - rec_loss: 0.0977 - kl_loss: 0.4904\n",
      "Epoch 708/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5875 - rec_loss: 0.0975 - kl_loss: 0.4900\n",
      "Epoch 709/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5899 - rec_loss: 0.0975 - kl_loss: 0.4924\n",
      "Epoch 710/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5874 - rec_loss: 0.0970 - kl_loss: 0.4904\n",
      "Epoch 711/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5886 - rec_loss: 0.0970 - kl_loss: 0.4917\n",
      "Epoch 712/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5897 - rec_loss: 0.0977 - kl_loss: 0.4920\n",
      "Epoch 713/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5867 - rec_loss: 0.0969 - kl_loss: 0.4898\n",
      "Epoch 714/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5887 - rec_loss: 0.0974 - kl_loss: 0.4913\n",
      "Epoch 715/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5877 - rec_loss: 0.0967 - kl_loss: 0.4910\n",
      "Epoch 716/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5859 - rec_loss: 0.0966 - kl_loss: 0.4893\n",
      "Epoch 717/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5878 - rec_loss: 0.0975 - kl_loss: 0.4903\n",
      "Epoch 718/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5891 - rec_loss: 0.0967 - kl_loss: 0.4924\n",
      "Epoch 719/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5862 - rec_loss: 0.0961 - kl_loss: 0.4901\n",
      "Epoch 720/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5876 - rec_loss: 0.0969 - kl_loss: 0.4907\n",
      "Epoch 721/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5857 - rec_loss: 0.0963 - kl_loss: 0.4893\n",
      "Epoch 722/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5857 - rec_loss: 0.0959 - kl_loss: 0.4897\n",
      "Epoch 723/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5850 - rec_loss: 0.0949 - kl_loss: 0.4901\n",
      "Epoch 724/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5862 - rec_loss: 0.0968 - kl_loss: 0.4895\n",
      "Epoch 725/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5868 - rec_loss: 0.0966 - kl_loss: 0.4902\n",
      "Epoch 726/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5856 - rec_loss: 0.0965 - kl_loss: 0.4891\n",
      "Epoch 727/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5875 - rec_loss: 0.0962 - kl_loss: 0.4914\n",
      "Epoch 728/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5891 - rec_loss: 0.0965 - kl_loss: 0.4927\n",
      "Epoch 729/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5847 - rec_loss: 0.0954 - kl_loss: 0.4893\n",
      "Epoch 730/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5861 - rec_loss: 0.0965 - kl_loss: 0.4896\n",
      "Epoch 731/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5855 - rec_loss: 0.0958 - kl_loss: 0.4898\n",
      "Epoch 732/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5869 - rec_loss: 0.0964 - kl_loss: 0.4905\n",
      "Epoch 733/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5869 - rec_loss: 0.0970 - kl_loss: 0.4899\n",
      "Epoch 734/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5844 - rec_loss: 0.0943 - kl_loss: 0.4901\n",
      "Epoch 735/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5860 - rec_loss: 0.0953 - kl_loss: 0.4907\n",
      "Epoch 736/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5855 - rec_loss: 0.0952 - kl_loss: 0.4903\n",
      "Epoch 737/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5852 - rec_loss: 0.0959 - kl_loss: 0.4893\n",
      "Epoch 738/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5836 - rec_loss: 0.0946 - kl_loss: 0.4890\n",
      "Epoch 739/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5840 - rec_loss: 0.0943 - kl_loss: 0.4897\n",
      "Epoch 740/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5838 - rec_loss: 0.0943 - kl_loss: 0.4895\n",
      "Epoch 741/1000\n",
      "97/97 [==============================] - 2s 23ms/step - tot_loss: 0.5817 - rec_loss: 0.0933 - kl_loss: 0.4884\n",
      "Epoch 742/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5832 - rec_loss: 0.0948 - kl_loss: 0.4884\n",
      "Epoch 743/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5854 - rec_loss: 0.0942 - kl_loss: 0.4912\n",
      "Epoch 744/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5838 - rec_loss: 0.0944 - kl_loss: 0.4894\n",
      "Epoch 745/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5858 - rec_loss: 0.0960 - kl_loss: 0.4898\n",
      "Epoch 746/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5828 - rec_loss: 0.0946 - kl_loss: 0.4882\n",
      "Epoch 747/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5839 - rec_loss: 0.0952 - kl_loss: 0.4888\n",
      "Epoch 748/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5835 - rec_loss: 0.0942 - kl_loss: 0.4893\n",
      "Epoch 749/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5849 - rec_loss: 0.0943 - kl_loss: 0.4906\n",
      "Epoch 750/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5830 - rec_loss: 0.0935 - kl_loss: 0.4895\n",
      "Epoch 751/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5837 - rec_loss: 0.0946 - kl_loss: 0.4890\n",
      "Epoch 752/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5842 - rec_loss: 0.0941 - kl_loss: 0.4901\n",
      "Epoch 753/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5827 - rec_loss: 0.0943 - kl_loss: 0.4884\n",
      "Epoch 754/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5845 - rec_loss: 0.0946 - kl_loss: 0.4899\n",
      "Epoch 755/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5814 - rec_loss: 0.0934 - kl_loss: 0.4880\n",
      "Epoch 756/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5817 - rec_loss: 0.0932 - kl_loss: 0.4885\n",
      "Epoch 757/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5828 - rec_loss: 0.0940 - kl_loss: 0.4888\n",
      "Epoch 758/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5818 - rec_loss: 0.0932 - kl_loss: 0.4886\n",
      "Epoch 759/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5859 - rec_loss: 0.0945 - kl_loss: 0.4914\n",
      "Epoch 760/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.5805 - rec_loss: 0.0926 - kl_loss: 0.4880\n",
      "Epoch 761/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5811 - rec_loss: 0.0917 - kl_loss: 0.4894\n",
      "Epoch 762/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.5836 - rec_loss: 0.0950 - kl_loss: 0.4886\n",
      "Epoch 763/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.5815 - rec_loss: 0.0925 - kl_loss: 0.4890\n",
      "Epoch 764/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5821 - rec_loss: 0.0929 - kl_loss: 0.4893\n",
      "Epoch 765/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5805 - rec_loss: 0.0916 - kl_loss: 0.4890\n",
      "Epoch 766/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5822 - rec_loss: 0.0931 - kl_loss: 0.4890\n",
      "Epoch 767/1000\n",
      "97/97 [==============================] - 2s 19ms/step - tot_loss: 0.5805 - rec_loss: 0.0922 - kl_loss: 0.4883\n",
      "Epoch 768/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5819 - rec_loss: 0.0930 - kl_loss: 0.4889\n",
      "Epoch 769/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5813 - rec_loss: 0.0921 - kl_loss: 0.4892\n",
      "Epoch 770/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5826 - rec_loss: 0.0937 - kl_loss: 0.4889\n",
      "Epoch 771/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5793 - rec_loss: 0.0915 - kl_loss: 0.4878\n",
      "Epoch 772/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5803 - rec_loss: 0.0930 - kl_loss: 0.4873\n",
      "Epoch 773/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5815 - rec_loss: 0.0925 - kl_loss: 0.4890\n",
      "Epoch 774/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5805 - rec_loss: 0.0917 - kl_loss: 0.4889\n",
      "Epoch 775/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5801 - rec_loss: 0.0917 - kl_loss: 0.4885\n",
      "Epoch 776/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5795 - rec_loss: 0.0924 - kl_loss: 0.4871\n",
      "Epoch 777/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5804 - rec_loss: 0.0914 - kl_loss: 0.4890\n",
      "Epoch 778/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5795 - rec_loss: 0.0914 - kl_loss: 0.4881\n",
      "Epoch 779/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5795 - rec_loss: 0.0917 - kl_loss: 0.4878\n",
      "Epoch 780/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5785 - rec_loss: 0.0906 - kl_loss: 0.4879\n",
      "Epoch 781/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5788 - rec_loss: 0.0910 - kl_loss: 0.4878\n",
      "Epoch 782/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5779 - rec_loss: 0.0909 - kl_loss: 0.4870\n",
      "Epoch 783/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5788 - rec_loss: 0.0910 - kl_loss: 0.4878\n",
      "Epoch 784/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5786 - rec_loss: 0.0905 - kl_loss: 0.4881\n",
      "Epoch 785/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5801 - rec_loss: 0.0917 - kl_loss: 0.4884\n",
      "Epoch 786/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5794 - rec_loss: 0.0914 - kl_loss: 0.4880\n",
      "Epoch 787/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5790 - rec_loss: 0.0913 - kl_loss: 0.4877\n",
      "Epoch 788/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5787 - rec_loss: 0.0907 - kl_loss: 0.4879\n",
      "Epoch 789/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5781 - rec_loss: 0.0908 - kl_loss: 0.4873\n",
      "Epoch 790/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5791 - rec_loss: 0.0906 - kl_loss: 0.4886\n",
      "Epoch 791/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5786 - rec_loss: 0.0913 - kl_loss: 0.4873\n",
      "Epoch 792/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5781 - rec_loss: 0.0910 - kl_loss: 0.4871\n",
      "Epoch 793/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5790 - rec_loss: 0.0912 - kl_loss: 0.4878\n",
      "Epoch 794/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5787 - rec_loss: 0.0912 - kl_loss: 0.4875\n",
      "Epoch 795/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5796 - rec_loss: 0.0909 - kl_loss: 0.4887\n",
      "Epoch 796/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5785 - rec_loss: 0.0905 - kl_loss: 0.4879\n",
      "Epoch 797/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5794 - rec_loss: 0.0909 - kl_loss: 0.4885\n",
      "Epoch 798/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5772 - rec_loss: 0.0900 - kl_loss: 0.4872\n",
      "Epoch 799/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5771 - rec_loss: 0.0894 - kl_loss: 0.4878\n",
      "Epoch 800/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5795 - rec_loss: 0.0907 - kl_loss: 0.4888\n",
      "Epoch 801/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5784 - rec_loss: 0.0904 - kl_loss: 0.4880\n",
      "Epoch 802/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5782 - rec_loss: 0.0908 - kl_loss: 0.4874\n",
      "Epoch 803/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5788 - rec_loss: 0.0902 - kl_loss: 0.4886\n",
      "Epoch 804/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5770 - rec_loss: 0.0897 - kl_loss: 0.4873\n",
      "Epoch 805/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5767 - rec_loss: 0.0900 - kl_loss: 0.4867\n",
      "Epoch 806/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5757 - rec_loss: 0.0893 - kl_loss: 0.4864\n",
      "Epoch 807/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5766 - rec_loss: 0.0893 - kl_loss: 0.4873\n",
      "Epoch 808/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5772 - rec_loss: 0.0900 - kl_loss: 0.4872\n",
      "Epoch 809/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5756 - rec_loss: 0.0881 - kl_loss: 0.4874\n",
      "Epoch 810/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5767 - rec_loss: 0.0896 - kl_loss: 0.4871\n",
      "Epoch 811/1000\n",
      "97/97 [==============================] - 2s 22ms/step - tot_loss: 0.5776 - rec_loss: 0.0902 - kl_loss: 0.4874\n",
      "Epoch 812/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5755 - rec_loss: 0.0893 - kl_loss: 0.4861\n",
      "Epoch 813/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5754 - rec_loss: 0.0876 - kl_loss: 0.4877\n",
      "Epoch 814/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5759 - rec_loss: 0.0894 - kl_loss: 0.4865\n",
      "Epoch 815/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5750 - rec_loss: 0.0883 - kl_loss: 0.4867\n",
      "Epoch 816/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5758 - rec_loss: 0.0893 - kl_loss: 0.4864\n",
      "Epoch 817/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5775 - rec_loss: 0.0901 - kl_loss: 0.4874\n",
      "Epoch 818/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5747 - rec_loss: 0.0881 - kl_loss: 0.4867\n",
      "Epoch 819/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5770 - rec_loss: 0.0902 - kl_loss: 0.4868\n",
      "Epoch 820/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5771 - rec_loss: 0.0891 - kl_loss: 0.4880\n",
      "Epoch 821/1000\n",
      "97/97 [==============================] - 2s 23ms/step - tot_loss: 0.5757 - rec_loss: 0.0893 - kl_loss: 0.4864\n",
      "Epoch 822/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5771 - rec_loss: 0.0895 - kl_loss: 0.4876\n",
      "Epoch 823/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5780 - rec_loss: 0.0894 - kl_loss: 0.4886\n",
      "Epoch 824/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5789 - rec_loss: 0.0891 - kl_loss: 0.4898\n",
      "Epoch 825/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5754 - rec_loss: 0.0886 - kl_loss: 0.4868\n",
      "Epoch 826/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5750 - rec_loss: 0.0881 - kl_loss: 0.4869\n",
      "Epoch 827/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5779 - rec_loss: 0.0895 - kl_loss: 0.4884\n",
      "Epoch 828/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5756 - rec_loss: 0.0876 - kl_loss: 0.4880\n",
      "Epoch 829/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5752 - rec_loss: 0.0894 - kl_loss: 0.4858\n",
      "Epoch 830/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5788 - rec_loss: 0.0900 - kl_loss: 0.4888\n",
      "Epoch 831/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5762 - rec_loss: 0.0892 - kl_loss: 0.4870\n",
      "Epoch 832/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5756 - rec_loss: 0.0882 - kl_loss: 0.4875\n",
      "Epoch 833/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5739 - rec_loss: 0.0880 - kl_loss: 0.4859\n",
      "Epoch 834/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5760 - rec_loss: 0.0879 - kl_loss: 0.4881\n",
      "Epoch 835/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5743 - rec_loss: 0.0886 - kl_loss: 0.4857\n",
      "Epoch 836/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5744 - rec_loss: 0.0875 - kl_loss: 0.4869\n",
      "Epoch 837/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5744 - rec_loss: 0.0878 - kl_loss: 0.4866\n",
      "Epoch 838/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5752 - rec_loss: 0.0883 - kl_loss: 0.4869\n",
      "Epoch 839/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5750 - rec_loss: 0.0878 - kl_loss: 0.4872\n",
      "Epoch 840/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5742 - rec_loss: 0.0883 - kl_loss: 0.4859\n",
      "Epoch 841/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5778 - rec_loss: 0.0890 - kl_loss: 0.4888\n",
      "Epoch 842/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5747 - rec_loss: 0.0886 - kl_loss: 0.4861\n",
      "Epoch 843/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5745 - rec_loss: 0.0872 - kl_loss: 0.4873\n",
      "Epoch 844/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5732 - rec_loss: 0.0874 - kl_loss: 0.4858\n",
      "Epoch 845/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5735 - rec_loss: 0.0874 - kl_loss: 0.4861\n",
      "Epoch 846/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5730 - rec_loss: 0.0869 - kl_loss: 0.4861\n",
      "Epoch 847/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5745 - rec_loss: 0.0889 - kl_loss: 0.4856\n",
      "Epoch 848/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5737 - rec_loss: 0.0872 - kl_loss: 0.4865\n",
      "Epoch 849/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5738 - rec_loss: 0.0868 - kl_loss: 0.4870\n",
      "Epoch 850/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5735 - rec_loss: 0.0864 - kl_loss: 0.4870\n",
      "Epoch 851/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5734 - rec_loss: 0.0875 - kl_loss: 0.4859\n",
      "Epoch 852/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5778 - rec_loss: 0.0871 - kl_loss: 0.4907\n",
      "Epoch 853/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5730 - rec_loss: 0.0871 - kl_loss: 0.4859\n",
      "Epoch 854/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5737 - rec_loss: 0.0865 - kl_loss: 0.4872\n",
      "Epoch 855/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5734 - rec_loss: 0.0870 - kl_loss: 0.4864\n",
      "Epoch 856/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5716 - rec_loss: 0.0860 - kl_loss: 0.4855\n",
      "Epoch 857/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5736 - rec_loss: 0.0873 - kl_loss: 0.4863\n",
      "Epoch 858/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5726 - rec_loss: 0.0875 - kl_loss: 0.4851\n",
      "Epoch 859/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5708 - rec_loss: 0.0856 - kl_loss: 0.4852\n",
      "Epoch 860/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5722 - rec_loss: 0.0855 - kl_loss: 0.4867\n",
      "Epoch 861/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5721 - rec_loss: 0.0865 - kl_loss: 0.4856\n",
      "Epoch 862/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5722 - rec_loss: 0.0861 - kl_loss: 0.4861\n",
      "Epoch 863/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5701 - rec_loss: 0.0854 - kl_loss: 0.4847\n",
      "Epoch 864/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5729 - rec_loss: 0.0873 - kl_loss: 0.4857\n",
      "Epoch 865/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5739 - rec_loss: 0.0868 - kl_loss: 0.4871\n",
      "Epoch 866/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5744 - rec_loss: 0.0875 - kl_loss: 0.4869\n",
      "Epoch 867/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5733 - rec_loss: 0.0868 - kl_loss: 0.4865\n",
      "Epoch 868/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5714 - rec_loss: 0.0861 - kl_loss: 0.4853\n",
      "Epoch 869/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5701 - rec_loss: 0.0853 - kl_loss: 0.4848\n",
      "Epoch 870/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5716 - rec_loss: 0.0857 - kl_loss: 0.4859\n",
      "Epoch 871/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5710 - rec_loss: 0.0854 - kl_loss: 0.4856\n",
      "Epoch 872/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5716 - rec_loss: 0.0864 - kl_loss: 0.4851\n",
      "Epoch 873/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5737 - rec_loss: 0.0870 - kl_loss: 0.4868\n",
      "Epoch 874/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5725 - rec_loss: 0.0875 - kl_loss: 0.4849\n",
      "Epoch 875/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5749 - rec_loss: 0.0857 - kl_loss: 0.4892\n",
      "Epoch 876/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5706 - rec_loss: 0.0858 - kl_loss: 0.4848\n",
      "Epoch 877/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5684 - rec_loss: 0.0848 - kl_loss: 0.4836\n",
      "Epoch 878/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5710 - rec_loss: 0.0858 - kl_loss: 0.4852\n",
      "Epoch 879/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5726 - rec_loss: 0.0864 - kl_loss: 0.4862\n",
      "Epoch 880/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5710 - rec_loss: 0.0856 - kl_loss: 0.4854\n",
      "Epoch 881/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5734 - rec_loss: 0.0873 - kl_loss: 0.4861\n",
      "Epoch 882/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5752 - rec_loss: 0.0857 - kl_loss: 0.4895\n",
      "Epoch 883/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5695 - rec_loss: 0.0850 - kl_loss: 0.4846\n",
      "Epoch 884/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5718 - rec_loss: 0.0858 - kl_loss: 0.4860\n",
      "Epoch 885/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5695 - rec_loss: 0.0847 - kl_loss: 0.4848\n",
      "Epoch 886/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5703 - rec_loss: 0.0861 - kl_loss: 0.4842\n",
      "Epoch 887/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5696 - rec_loss: 0.0846 - kl_loss: 0.4850\n",
      "Epoch 888/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5721 - rec_loss: 0.0867 - kl_loss: 0.4854\n",
      "Epoch 889/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5697 - rec_loss: 0.0849 - kl_loss: 0.4848\n",
      "Epoch 890/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5702 - rec_loss: 0.0852 - kl_loss: 0.4850\n",
      "Epoch 891/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5709 - rec_loss: 0.0863 - kl_loss: 0.4846\n",
      "Epoch 892/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5699 - rec_loss: 0.0844 - kl_loss: 0.4855\n",
      "Epoch 893/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5714 - rec_loss: 0.0857 - kl_loss: 0.4858\n",
      "Epoch 894/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5687 - rec_loss: 0.0846 - kl_loss: 0.4842\n",
      "Epoch 895/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5706 - rec_loss: 0.0841 - kl_loss: 0.4865\n",
      "Epoch 896/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5695 - rec_loss: 0.0848 - kl_loss: 0.4847\n",
      "Epoch 897/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5691 - rec_loss: 0.0847 - kl_loss: 0.4844\n",
      "Epoch 898/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5702 - rec_loss: 0.0843 - kl_loss: 0.4860\n",
      "Epoch 899/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5694 - rec_loss: 0.0839 - kl_loss: 0.4855\n",
      "Epoch 900/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5690 - rec_loss: 0.0849 - kl_loss: 0.4841\n",
      "Epoch 901/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5718 - rec_loss: 0.0849 - kl_loss: 0.4869\n",
      "Epoch 902/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5688 - rec_loss: 0.0842 - kl_loss: 0.4846\n",
      "Epoch 903/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5677 - rec_loss: 0.0835 - kl_loss: 0.4842\n",
      "Epoch 904/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5684 - rec_loss: 0.0845 - kl_loss: 0.4840\n",
      "Epoch 905/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5687 - rec_loss: 0.0844 - kl_loss: 0.4843\n",
      "Epoch 906/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5664 - rec_loss: 0.0830 - kl_loss: 0.4834\n",
      "Epoch 907/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5673 - rec_loss: 0.0833 - kl_loss: 0.4840\n",
      "Epoch 908/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5691 - rec_loss: 0.0847 - kl_loss: 0.4844\n",
      "Epoch 909/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5674 - rec_loss: 0.0846 - kl_loss: 0.4828\n",
      "Epoch 910/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5691 - rec_loss: 0.0840 - kl_loss: 0.4851\n",
      "Epoch 911/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5672 - rec_loss: 0.0836 - kl_loss: 0.4837\n",
      "Epoch 912/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5697 - rec_loss: 0.0850 - kl_loss: 0.4847\n",
      "Epoch 913/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5694 - rec_loss: 0.0837 - kl_loss: 0.4857\n",
      "Epoch 914/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5671 - rec_loss: 0.0840 - kl_loss: 0.4831\n",
      "Epoch 915/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5667 - rec_loss: 0.0828 - kl_loss: 0.4839\n",
      "Epoch 916/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5674 - rec_loss: 0.0839 - kl_loss: 0.4835\n",
      "Epoch 917/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5653 - rec_loss: 0.0823 - kl_loss: 0.4830\n",
      "Epoch 918/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5671 - rec_loss: 0.0836 - kl_loss: 0.4835\n",
      "Epoch 919/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5681 - rec_loss: 0.0838 - kl_loss: 0.4843\n",
      "Epoch 920/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5686 - rec_loss: 0.0831 - kl_loss: 0.4855\n",
      "Epoch 921/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5685 - rec_loss: 0.0833 - kl_loss: 0.4852\n",
      "Epoch 922/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5671 - rec_loss: 0.0835 - kl_loss: 0.4836\n",
      "Epoch 923/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5667 - rec_loss: 0.0824 - kl_loss: 0.4843\n",
      "Epoch 924/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5659 - rec_loss: 0.0834 - kl_loss: 0.4825\n",
      "Epoch 925/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5667 - rec_loss: 0.0824 - kl_loss: 0.4844\n",
      "Epoch 926/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5660 - rec_loss: 0.0824 - kl_loss: 0.4836\n",
      "Epoch 927/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5663 - rec_loss: 0.0839 - kl_loss: 0.4823\n",
      "Epoch 928/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5674 - rec_loss: 0.0834 - kl_loss: 0.4840\n",
      "Epoch 929/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5677 - rec_loss: 0.0830 - kl_loss: 0.4847\n",
      "Epoch 930/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5671 - rec_loss: 0.0833 - kl_loss: 0.4838\n",
      "Epoch 931/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5686 - rec_loss: 0.0834 - kl_loss: 0.4852\n",
      "Epoch 932/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5666 - rec_loss: 0.0824 - kl_loss: 0.4842\n",
      "Epoch 933/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5676 - rec_loss: 0.0833 - kl_loss: 0.4843\n",
      "Epoch 934/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5673 - rec_loss: 0.0837 - kl_loss: 0.4836\n",
      "Epoch 935/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5700 - rec_loss: 0.0833 - kl_loss: 0.4867\n",
      "Epoch 936/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5664 - rec_loss: 0.0822 - kl_loss: 0.4842\n",
      "Epoch 937/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5666 - rec_loss: 0.0828 - kl_loss: 0.4838\n",
      "Epoch 938/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5669 - rec_loss: 0.0825 - kl_loss: 0.4844\n",
      "Epoch 939/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5664 - rec_loss: 0.0828 - kl_loss: 0.4836\n",
      "Epoch 940/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5656 - rec_loss: 0.0830 - kl_loss: 0.4826\n",
      "Epoch 941/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5680 - rec_loss: 0.0822 - kl_loss: 0.4858\n",
      "Epoch 942/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5657 - rec_loss: 0.0823 - kl_loss: 0.4834\n",
      "Epoch 943/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5658 - rec_loss: 0.0822 - kl_loss: 0.4836\n",
      "Epoch 944/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5650 - rec_loss: 0.0821 - kl_loss: 0.4829\n",
      "Epoch 945/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5649 - rec_loss: 0.0819 - kl_loss: 0.4830\n",
      "Epoch 946/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5643 - rec_loss: 0.0824 - kl_loss: 0.4819\n",
      "Epoch 947/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5653 - rec_loss: 0.0820 - kl_loss: 0.4833\n",
      "Epoch 948/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5630 - rec_loss: 0.0813 - kl_loss: 0.4817\n",
      "Epoch 949/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5641 - rec_loss: 0.0816 - kl_loss: 0.4826\n",
      "Epoch 950/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5661 - rec_loss: 0.0821 - kl_loss: 0.4840\n",
      "Epoch 951/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5635 - rec_loss: 0.0805 - kl_loss: 0.4830\n",
      "Epoch 952/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5662 - rec_loss: 0.0818 - kl_loss: 0.4843\n",
      "Epoch 953/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5639 - rec_loss: 0.0810 - kl_loss: 0.4829\n",
      "Epoch 954/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5651 - rec_loss: 0.0833 - kl_loss: 0.4818\n",
      "Epoch 955/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5638 - rec_loss: 0.0815 - kl_loss: 0.4823\n",
      "Epoch 956/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5631 - rec_loss: 0.0810 - kl_loss: 0.4821\n",
      "Epoch 957/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5648 - rec_loss: 0.0815 - kl_loss: 0.4833\n",
      "Epoch 958/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5615 - rec_loss: 0.0805 - kl_loss: 0.4810\n",
      "Epoch 959/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5651 - rec_loss: 0.0822 - kl_loss: 0.4829\n",
      "Epoch 960/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5649 - rec_loss: 0.0817 - kl_loss: 0.4832\n",
      "Epoch 961/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5631 - rec_loss: 0.0827 - kl_loss: 0.4804\n",
      "Epoch 962/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5629 - rec_loss: 0.0810 - kl_loss: 0.4819\n",
      "Epoch 963/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5635 - rec_loss: 0.0807 - kl_loss: 0.4828\n",
      "Epoch 964/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5647 - rec_loss: 0.0814 - kl_loss: 0.4832\n",
      "Epoch 965/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5641 - rec_loss: 0.0808 - kl_loss: 0.4833\n",
      "Epoch 966/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5640 - rec_loss: 0.0825 - kl_loss: 0.4815\n",
      "Epoch 967/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5636 - rec_loss: 0.0815 - kl_loss: 0.4821\n",
      "Epoch 968/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5630 - rec_loss: 0.0806 - kl_loss: 0.4824\n",
      "Epoch 969/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5624 - rec_loss: 0.0815 - kl_loss: 0.4809\n",
      "Epoch 970/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5633 - rec_loss: 0.0803 - kl_loss: 0.4829\n",
      "Epoch 971/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5621 - rec_loss: 0.0808 - kl_loss: 0.4813\n",
      "Epoch 972/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5625 - rec_loss: 0.0804 - kl_loss: 0.4821\n",
      "Epoch 973/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5612 - rec_loss: 0.0806 - kl_loss: 0.4806\n",
      "Epoch 974/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5647 - rec_loss: 0.0803 - kl_loss: 0.4844\n",
      "Epoch 975/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5635 - rec_loss: 0.0818 - kl_loss: 0.4817\n",
      "Epoch 976/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5635 - rec_loss: 0.0809 - kl_loss: 0.4826\n",
      "Epoch 977/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5629 - rec_loss: 0.0813 - kl_loss: 0.4816\n",
      "Epoch 978/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5614 - rec_loss: 0.0809 - kl_loss: 0.4805\n",
      "Epoch 979/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5643 - rec_loss: 0.0810 - kl_loss: 0.4833\n",
      "Epoch 980/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5612 - rec_loss: 0.0796 - kl_loss: 0.4816\n",
      "Epoch 981/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5611 - rec_loss: 0.0808 - kl_loss: 0.4803\n",
      "Epoch 982/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5615 - rec_loss: 0.0796 - kl_loss: 0.4819\n",
      "Epoch 983/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5626 - rec_loss: 0.0800 - kl_loss: 0.4826\n",
      "Epoch 984/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5643 - rec_loss: 0.0815 - kl_loss: 0.4828\n",
      "Epoch 985/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5636 - rec_loss: 0.0814 - kl_loss: 0.4821\n",
      "Epoch 986/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5625 - rec_loss: 0.0804 - kl_loss: 0.4821\n",
      "Epoch 987/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5639 - rec_loss: 0.0808 - kl_loss: 0.4831\n",
      "Epoch 988/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5631 - rec_loss: 0.0807 - kl_loss: 0.4825\n",
      "Epoch 989/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5629 - rec_loss: 0.0807 - kl_loss: 0.4823\n",
      "Epoch 990/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5611 - rec_loss: 0.0796 - kl_loss: 0.4816\n",
      "Epoch 991/1000\n",
      "97/97 [==============================] - 2s 21ms/step - tot_loss: 0.5626 - rec_loss: 0.0810 - kl_loss: 0.4817\n",
      "Epoch 992/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5604 - rec_loss: 0.0785 - kl_loss: 0.4819\n",
      "Epoch 993/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5609 - rec_loss: 0.0799 - kl_loss: 0.4810\n",
      "Epoch 994/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5624 - rec_loss: 0.0798 - kl_loss: 0.4826\n",
      "Epoch 995/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5614 - rec_loss: 0.0812 - kl_loss: 0.4802\n",
      "Epoch 996/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5595 - rec_loss: 0.0791 - kl_loss: 0.4804\n",
      "Epoch 997/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5598 - rec_loss: 0.0793 - kl_loss: 0.4805\n",
      "Epoch 998/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5599 - rec_loss: 0.0789 - kl_loss: 0.4810\n",
      "Epoch 999/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5609 - rec_loss: 0.0797 - kl_loss: 0.4812\n",
      "Epoch 1000/1000\n",
      "97/97 [==============================] - 2s 20ms/step - tot_loss: 0.5605 - rec_loss: 0.0796 - kl_loss: 0.4809\n",
      "1973.2205448150635\n"
     ]
    }
   ],
   "source": [
    "# Train the model using single set of hyperparameters\n",
    "HH_IDX = len(CAT_LENGTHS)  # Number of household variables (used in CVAE)\n",
    "start_time = time.time()\n",
    "\n",
    "vae_model = vae.VAE(MANIFEST_DIM, HIDDEN_DIM, LATENT_DIM, CAT_LENGTHS, HH_IDX, KL_WEIGHT)\n",
    "vae_model.compile(optimizer=keras.optimizers.Adam())\n",
    "history = vae_model.fit(train_data, epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=[validationCallback()])\n",
    "\n",
    "train_time = (time.time()-start_time)\n",
    "print(train_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot model loss/training progress\n",
    "plt.plot(history.history['tot_loss'])\n",
    "plt.plot(history.history['rec_loss'])\n",
    "plt.plot(history.history['kl_loss'])\n",
    "plt.plot(val_epochs, val_loss)\n",
    "plt.title(\"Training History\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Total\",\"Reconstruction\",\"KLD\",\"Validation\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-20 12:25:21.930990: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get latent vars from the encoder; feed to decoder and get sampled manifest variables\n",
    "z_mean, z_logvar, z = vae_model.encoder.predict(test_data[:,:MANIFEST_DIM])\n",
    "\n",
    "# Show distributions of the resulting variables\n",
    "fig, axes = plt.subplots(nrows=1, ncols=LATENT_DIM, figsize=(10,3))\n",
    "fig.tight_layout(w_pad=2, h_pad=10)\n",
    "\n",
    "for i in range(LATENT_DIM):\n",
    "    sns.histplot(z[:,i], ax=axes[i], bins=50, stat=\"probability\")\n",
    "    axes[i].set_title(f\"Latent Variable {i+1} in Test Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-20 12:25:23.842454: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Variable Loss: 0.0725640058517456\n"
     ]
    }
   ],
   "source": [
    "# Draw predictions from test data\n",
    "results = vae_model.predict(test_data)\n",
    "loss_cat = vae.get_reconstruction_loss(test_data, results, CAT_LENGTHS)\n",
    "print(f\"Categorical Variable Loss: {loss_cat}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the testing data from ohe to values\n",
    "test_data_df = pd.DataFrame()\n",
    "for i, x in enumerate(results[0]):\n",
    "    result = np.apply_along_axis(DataProcessor.logit_to_val, 1, x, pums_encodings_list[i])\n",
    "    result_df = pd.DataFrame(result).add_prefix(f\"{VAR_NAMES[i]}_\")\n",
    "    test_data_df = pd.concat([test_data_df, result_df], axis=1)\n",
    "test_data_df.columns = VAR_NAMES\n",
    "\n",
    "# Convert the test results from logits to values\n",
    "test_results_df = pd.DataFrame()\n",
    "for i, x in enumerate(results[0]):\n",
    "    result = np.apply_along_axis(DataProcessor.logit_to_val, 1, x, pums_encodings_list[i])\n",
    "    result_df = pd.DataFrame(result).add_prefix(f\"{VAR_NAMES[i]}_\")\n",
    "    test_results_df = pd.concat([test_results_df, result_df], axis=1)\n",
    "test_results_df.columns = VAR_NAMES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Synthetic Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-20 12:25:31.337297: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 6.4759910106658936 seconds ---\n"
     ]
    }
   ],
   "source": [
    "# How many synthetic households to generate\n",
    "POP_SIZE = 100000\n",
    "\n",
    "# Generate random normal sample to represent each latent variable, for each row (different hh per row)\n",
    "inputs = np.random.normal(loc=0, scale=1, size=(POP_SIZE, LATENT_DIM))\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Generate households; each hh has unique latent input\n",
    "results = vae_model.decoder.predict([inputs, inputs[:,LATENT_DIM:]])  # Nothing is actually in the second input\n",
    "\n",
    "print(f\"--- {time.time() - start_time} seconds ---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Save raw output values to be conditional inputs for the person CVAE\n",
    "synth_hh_ohe_df = pd.DataFrame()\n",
    "synth_hh_df = pd.DataFrame()\n",
    "for i, x in enumerate(results[0]):\n",
    "    result = np.apply_along_axis(DataProcessor.logit_to_ohe, 1, x)  # Most variables in PUMS are indexed from 1\n",
    "    result_df = pd.DataFrame(result).add_prefix(f\"{VAR_NAMES[i]}_\")\n",
    "    synth_hh_ohe_df = pd.concat([synth_hh_ohe_df, result_df], axis=1)\n",
    "    \n",
    "    result = np.apply_along_axis(DataProcessor.logit_to_val, 1, x, pums_encodings_list[i])\n",
    "    result_df = pd.DataFrame(result).add_prefix(f\"{VAR_NAMES[i]}_\")\n",
    "    synth_hh_df = pd.concat([synth_hh_df, result_df], axis=1)\n",
    "\n",
    "synth_hh_df.columns = VAR_NAMES\n",
    "\n",
    "synth_hh_df.to_csv('data/synthetic_populations/wa_households.csv', index=False)\n",
    "synth_hh_ohe_df.to_csv('data/synthetic_populations/wa_households_raw.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mqXbf3jZjM7YFXl/tHtgkaUE3Yu4JI4BVjgHoHXH0hhigd8TRG2KA3hFHb4gBoNs3llQziWepdt/RNlnaJgtLqt1Lui8t75Ybx9C74ugNMfSWOHpDDL0ljt4QQ2sc3d1HNZN4lmr3HW3TL0NbM7OGV80+8e3V7iX1I6l2f1ObbW4CPpqOUnk7sCYilmVsa2bW8Kp2JZ6x2v1M4ATgCWADcEa5thkOO73nP0mnOYbX9IY4ekMM0Dvi6A0xQO+IozfEAD0QhyLa7Wo2M7MC8B2bZmYF5iRuZlZgdZHEe8Mt+pJ+LmlFnuPUJe0m6a+SHpG0UNK5OcQwQNIcSQ+mMXyz1jG0iaevpAck/T6n4y+WNF/SvJ4YTtaNOIZJuk7SovT8eEeNj//W9DtofayV9LlaxlASy+fTc3OBpGslDcghhnPT4y/s9vcQEYV+kPzw+SSwJ8nQxAeBCTnEcTRwKLAgx+9iDHBo+now8FitvwuSMf47pq+bgXuAt+f4nXwBuAb4fU7HXwyMyOvzl8RxOXBm+rofMCzHWPoCLwC753DsscDTwMD0/a+Bj9c4hv2BBcAOJINL/gTs1dX91cOVeK+4RT8iZgEv1vq4bWJYFhH3p6/XAY+QnLS1jCEiYn36tjl95PLruaRdgfcCl+Rx/N5C0hCSi4xLASJic0S8nGNI7waejIhncjp+EzBQUhNJIq31PSj7ArMjYkNEbAX+Bry/qzurhyTe0a37DU3SeOAQkivhWh+7r6R5wArgtoioeQyp7wP/DmzL6fiQ/A/sVklz0yki8rAnsBL4Rdq1dImkQTnFAsl9H9fmceCIeB74b+BZYBnJvSm31jiMBcDRkoZL2oFkmPVuFdp0qB6SeOZb9BuFpB2B64HPRcTaWh8/Iloi4mCSO20Pl7R/rWOQdCKwIiLm1vrYbUyKiENJZuQ8W9LROcTQRNLV95OIOAR4Bcjrt6N+wMnAb3I6/k4kf6nvAewCDJJ0ei1jiIhHgO8CtwG3kHQBb+3q/uohiWe5vb9hSGomSeBXR8QNecaS/sl+OzA5h8NPAk6WtJiki+1dkq6qdRARsTR9XgHMIOn+q7UlwJKSv4iuI0nqeZgC3B8Ry3M6/nuApyNiZURsAW4Ajqx1EBFxaUQcGhFHk3TDPt7VfdVDEvct+ilJIun3fCQiLswphpGShqWvB5L8o1lU6zgi4isRsWtEjCc5J/4SETW94pI0SNLg1tfA8SR/StdURLwAPCepdca8dwMP1zqO1Knk1JWSehZ4u6Qd0n8v7yb57aimJI1Kn8cB/0Q3vpPCV7uPrt+i36MkXQscA4yQtAT4ekRcWuMwJgEfAeanfdIA/xERM2sYwxjg8rSwRx/g1xGRy/C+XmA0MCPJFTQB10TELTnF8hng6vRC5ynSKS5qKe3/PQ74ZK2P3Soi7pF0HXA/SRfGA+RzC/71koYDW4CzI+Klru7It92bmRVYPXSnmJk1LCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNwMkLZa0XNKgkmVnSro9fR2SXpG0XtLzki6U1De3gM1STuJmr2kCzi2z/qCI2BF4N/Bh4F9qEpVZGU7iZq/5HvBFScPKbRQRi4A7gP1rEZRZOU7iZq+5D7gd+GK5jSRNAP4BeKAGMZmV1ZR3AGa9zPnAXZJ+0M66+yW1AC8ClwC/qGlkZu1wEjcrERELJP0eOA94pM3qQyPiiRzCMuuQu1PM3ujrJD9ajs07ELNKnMTN2kivtn8FfDbvWMwqcRI3a98FwKCKW5nlTBGRdwxmZtZFvhI3MyuwqiVxST+XtELSgg7WS9JFkp6Q9JCkQ0vWTZb0aLruvGrFaGZWdNW8Er8MmFxm/RRgr/RxFvATgHQ+ih+n6ycAp6Y3V5iZWRtVS+IRMYvkpoiOTAWuiMRsYJikMcDhwBMR8VREbAZ+mW5rZmZt5Hmzz1jguZL3S9Jl7S0/oqOdSDqL5EqeQYMGvW2fffbp+Ujr3NqNW9i0ZVuX2rZEsHLdpm4dvw/bEF37gX0n1jNGq7t1/J6wrcL1UKAaRUI732Ttjt1Wuc8tgiZa2KzmGkbUcwJYqRFs0A5dat9HYs1zj66KiJHdiSPPJN7ef90os7xdETEdmA4wceLEuO+++3omugZywNf/yOZNW7vUtongP/rewlE7b2PsTgM73X7IusfZZfnfunTs1+wIH7gUBgwFVHIGCaR2nruyrsw+37Qn9N+xm5/BGpGkZ7q7jzyT+BJgt5L3uwJLgX4dLLcO3PTgUm5/dEWX25/Zci2TR6/gLSM7n4i0/gX6vPAgrAZe7tf5g7dsTp6P/AwM3a38th0Zsgvse1LX2poVXJ5J/CbgHEm/JOkuWRMRyyStBPaStAfwPDCNZO5m68D0WU/y+PL1jBzcv0vt/0/TTPps3JG+r+zahdYBux4OJ/0ARvv3Z7Naq1oSl3QtcAwwQtISkvkomgEi4qfATOAE4AlgA3BGum6rpHOAPwJ9gZ9HxMJqxVkvjnrLCC79+GFda/x/+sLbPgzHf6tngzKzqqtaEo+IUyusD+DsDtbNJEnylsHIlhUcuGEpPFZuMFAZWzb0bEBmVjOeirYX2NKyjcWrXuly+3Ne+i/epkVwTTeC6D+0G43NLC9O4r3A//nDI1x29+Iut/9dv408MfAA3vKR9uoYZKA+MNqVxsyKyEm8F9jjhZv5/YBfdmmIHsBOLy1m45jjYOzbejgyM+vtnMR7gf3W/5238AwDhh/dtR0Mfw8DDvYAHrNG5CTeS6zScHY9/fq8wzCzgvFUtGZmBeYkbmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmAeYtgDnlixnkvvfIqWbV0rbHDkSxvY2f8lzKwLnDp6wMz5y7h2znPsPGTAa3UFOuFdTX0Z0Ny35wMzs7pX1SQuaTLwA5IpZS+JiO+0Wf8l4LSSWPYFRkbEi5IWA+uAFmBrREysZqw94a7z3kXfPl3I4tdfC88/3/MBmVndq+Z84q1V648jqeJzr6SbIuLh1m0i4nvA99LtTwI+HxGl86keGxGrqhWjmVnRZU7iko4Expe2iYgryjTZXrU+bd9atf7hDrY/Fbg2azxmZpYxiUu6EngzMI+kewOS4sXlknjmqvWSdgAmA+eULA7gVkkB/CwtiNxe2+3V7seNG1fpo5iZ1ZWsV+ITgQlpNZ6sOlO1/iTgrjZdKZMiYqmkUcBtkhZFxKw37LBNtftOxGdmVnhZx4kvAHbu5L47qmbfnmm06UqJiKXp8wpgBkn3jJmZlch6JT4CeFjSHGBT68KIOLlMm3vJULVe0lDgncDpJcsGAX0iYl36+njggoyxmpk1jKxJ/Bud3XFHVeslfSpd/9N00/cDt0ZEaZHJ0cAMJYOum4BrIuKWzsZQK/23rOGDfW9H969svxOpkvm/gZ3G93BUZtYIMiXxiPibpNHAYemiOWk3R6V2b6haX5K8W99fBlzWZtlTwEFZYusN9n9hBp9sng6/78ZOBu/SY/GYWePIOjrlQyTjuW8nudb8oaQvRcR1VYytMPrEVgBazp1P375dHHq/w/AejMjMGkXWjPNV4LDWq29JI4E/AU7ipQbvAk2eycDMaidrxunTpvtkNXU0A+JzL27gvBseYuOWbV1qf+SSVbyjuYeDMjPLIGsSv0XSH3ltGOA/06avu8geWrKGu55YzSHjhjGoX+evpHcfvgOspWvzppiZdUPWHza/JOkDwCSSPvHpETGjqpHl4LsfOJC9Rw/ufMO//Q3+2vPxmJlVkvmyMyKuB66vYixmZtZJZZO4pDsj4ihJ63j9LfMCIiKGVDU6MzMrq2wSj4ij0ucu9DGYmVm1ZRphks5iWHGZmZnVVtZhgvuVvpHUBLyt58MxM7POKJvEJX0l7Q8/UNLa9LEOWA78tiYRmplZh8om8Yj4NjAUuCIihqSPwRExPCK+UpsQzcysIxWHGEbENkldmowqQ6HkY0iu6J9OF90QERdkadujYhsH6CkGLu0PG3fofPtn7u75mMzMMsg6Tny2pMMi4t6sO85SKDl1R0Sc2MW2PWLk8rv4Xf+vda+DqHkHujYPrZlZ12VN4scCn5T0DPAKr40TP7BMm84WSu6ptp3Wd2sylfkLx3yPncft3bWdDBkLfepmOhkzK4isSXxKF/adtVDyOyQ9SFK67YsRsbATbXu0UPLG0YfCnhO7tQ8zs1rKdOkYEc8Aw0gKGp8EDEuXlZOlUPL9wO4RcRDwQ+DGTrRtjW16REyMiIkjR46sEJKZWX3JerPPucDVwKj0cZWkz1RoVrFQckSsjYj16euZQLOkEVnamplZ9u6UTwBHtNbBlPRd4O8kV88dqVgoWdLOwPKICEmHk/xPZTXwcqW2ZmaWPYkLaCl530KFoRgZCyWfAnxa0lbgVWBaRATQbttOfC4zs4aQNYn/ArhH0gyS5D0VuLRSo0qFkiPiR8CPsrY1M7PXy1oU4kJJtwNHpYvOiIgHqhaVmZll0tmBzSIZJeK7WszMeoGso1POBy4HdgJGAL+Q9LVqBmZmZpVl7RM/FTgkIjYCSPoOyRjvb1UrMDMzqyxrd8piYEDJ+/7Akz0ejZmZdUrWK/FNwEJJt5H0iR8H3CnpIoCI+GyV4jMzszKyJvEZ6aPV7T0fipmZdVbWIYaXS+oHtE7x92hEbKleWGZmlkWmJJ4Wb7icpG9cwG6SPhYRs6oWmZmZVZS1O+V/gOMj4lEASXsD1+JiyWZmuco6OqW5NYEDRMRjQHN1QjIzs6yyXonPlXQpcGX6/jRgbnVCMjOzrLIm8U8BZwOfJekTnwVcXK2gzMwsm4pJXFIfYG5E7A9c2JmdZ6h2fxrw5fTteuDTEfFgum4xsI5k2tutEeG6aWZmbVRM4hGxTdKDksZFxLNZd5yxYv3TwDsj4iVJU4DpvL6W5rERsSrrMZ9YsZ6pP74r6+bbjX3+Kd7Wr9PNzMxyl7U7ZQzJHZtzSKrdAxARJ5dpU7FifUTcXbL9bJIybF0WwLCBnf+9dcIuQ2AVjBk6oPLGZma9SNYk/s0u7DtzxfrUJ4CbS94HcKukAH4WEdPba1Ra7X7UruO5/H8d3vlIFz4Pv4H+TX0739bMLEdlk7ikASQ/ar4FmA9cGhFbM+47c8V6SceSJPGjShZPioilkkYBt0la1N7NRWlynw6w134Htbt/M7N6VelK/HJgC3AHMAWYAJybcd+ZKtZLOhC4BJgSEatbl0fE0vR5RVoW7nCSUTEdGvDqC3BrF6Y5f/imzrcxM+sFKiXxCRFxAEA6TnxOJ/adpdr9OOAG4CPpDUStywcBfSJiXfr6eOCCSgfsv2k13Fux9OcbtWyGIWNhx9Gdb2tmlqNKSXz7JFdp9frMO85Y7f58YDhwcbrv1qGEo4EZ6bIm4JqIuKXSMTcM2g2++nTmGM3Mik4RHXcjS2rhtdEoAgYCG9LXERFDqh5hJ0zYe494+DEncTMrBklzu3sPTNkr8YjwcA0zs16ss9XuzcysF3ESNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczKzAncTOzAnMSNzMrMCdxM7MCcxI3MyswJ3EzswJzEjczK7CqJnFJkyU9KukJSee1s16SLkrXPyTp0Kxtzcysikm8pNp9a0WgUyVNaLPZFGCv9HEW8JNOtDUza3jVvBLfXu0+IjYDrdXuS00FrojEbGCYpDEZ25qZNbys1e67Iku1+/a2GZuxLfD6avfAekmPdiPmckYAq6q0757g+LquN8cGvTu+3hwb9P743trdHVQziWepdt/RNlnaJgtLqt1Xk6T7uluBo5ocX9f15tigd8fXm2ODYsTX3X1UM4lnqXbf0Tb9MrQ1M2t41ewT317tXlI/kmr3N7XZ5ibgo+kolbcDayJiWca2ZmYNr2pX4hmr3c8ETgCeICnAfEa5ttWKNaOqd9l0k+Prut4cG/Tu+HpzbNAA8ZWtdm9mZr2b79g0MyswJ3EzswJzEi8haTdJf5X0iKSFks5tZ5tjJK2RNC99nF/jGBdLmp8e+w3Dk8pNZVDluN5a8p3Mk7RW0ufabFPT707SzyWtkLSgZNmbJN0m6fH0eacO2lZ92ocO4vuepEXpf7sZkoZ10LbseVCl2L4h6fmS/34ndNA2r+/uVyWxLZY0r4O21f7u2s0jVTv3IsKP9AGMAQ5NXw8GHgMmtNnmGOD3Oca4GBhRZv0JwM0kY+3fDtyTQ4x9gReA3fP87oCjgUOBBSXL/gs4L319HvDdDuJ/EtiTZLjrg23PgyrGdzzQlL7+bnvxZTkPqhTbN4AvZvhvn8t312b9/wDn5/TdtZtHqnXu+Uq8REQsi4j709frgEdI7h4tko6mMqildwNPRsQzNT7u60TELODFNounApenry8H3tdO05pM+9BefBFxa0RsTd/OJrlHouY6+O6yyO27ayVJwIeAa3v6uFmUySNVOfecxDsgaTxwCHBPO6vfIelBSTdL2q+2kRHArZLmKplyoK2OpjKopWl0/A8oz+8OYHQk9yKQPo9qZ5ve8B0C/C+Sv6raU+k8qJZz0q6en3fQHdAbvrt/AJZHxOMdrK/Zd9cmj1Tl3HMSb4ekHYHrgc9FxNo2q+8n6SY4CPghcGONw5sUEYeSzPB4tqSj26zPPGVBNaQ3Z50M/Kad1Xl/d1nl+h0CSPoqsBW4uoNNKp0H1fAT4M3AwcAyki6LtnL/7oBTKX8VXpPvrkIe6bBZO8vKfn9O4m1Iaib54q+OiBvaro+ItRGxPn09E2iWNKJW8UXE0vR5BTCD5M+vUlmmO6imKcD9EbG87Yq8v7vU8tbupfR5RTvb5PodSvoYcCJwWqQdpW1lOA96XEQsj4iWiNgG/L8Ojpn3d9cE/BPwq462qcV310Eeqcq55yReIu1LuxR4JCIu7GCbndPtkHQ4yXe4ukbxDZI0uPU1yY9gC9ps1tFUBrXS4VVQnt9diZuAj6WvPwb8tp1tcpv2QdJk4MvAyRGxoYNtspwH1Yit9LeV93dwzLynzHgPsCgilrS3shbfXZk8Up1zr1q/0BbxARxF8qfLQ8C89HEC8CngU+k25wALSX41ng0cWcP49kyP+2Aaw1fT5aXxiaSgxpPAfGBiDePbgSQpDy1Zltt3R/I/k2XAFpIrnE8Aw4E/A4+nz29Kt90FmFnS9gSSUQVPtn7PNYrvCZI+0dbz76dt4+voPKhBbFem59RDaWIZ05u+u3T5Za3nW8m2tf7uOsojVTn3fNu9mVmBuTvFzKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEncGo6kP0q6oJ3lUyW9IOkqSZslrS95PJhuM15SSGpq0/YySd+q1Wcwa+Ukbo3oMuAjktRm+UeAq4GtwH9FxI4lj4NqHaRZFk7i1ohuBN4E/EPrAkk7AScCV+QUk1mXOIlbw4mIV4FfAx8tWfwhYFFEPJhPVGZd4yRujepy4IOSBqbvP5oua/VFSS+XPC5v035V6XrgwzWI2ewNmipvYlZ/IuJOSSuBqZLmAIcB/1SyyX9HxNfK7GJERGxtfSPpsupEalaek7g1sitIrsDfCtwaEctzjses09ydYo3sCuA9wL/w+q4Us8JwEreGFRGLgbuBQcBNbVb/e5tx4qtqHqBZBoqIvGMwM7Mu8pW4mVmBVS2JS/q5pBWSFnSwXpIukvSEpIckHVqybrKkR9N151UrRjOzoqvmlfhlwOQy66cAe6WPs4CfAEjqC/w4XT8BOFXShCrGaWZWWFVL4hExC3ixzCZTgSsiMRsYJmkMcDjwREQ8FRGbgV+m25qZWRt5jhMfCzxX8n5Juqy95Ud0tBNJZ5FcyTNo0KC37bPPPj0fqZn1Chs2t/DkyvV5h9Ft/djCW/Ucc5dtWxURI7uzrzyTeNsZ5ACizPJ2RcR0YDrAxIkT47777uuZ6MzqyKr1m/j9g0tpKfhgtD8ueIENi1/kzKP2YPSQAXmH02VDNzzDh2a/D31z7TPd3VeeSXwJsFvJ+12BpUC/DpabWRf96t7n+N4fH807jB7Rv6kPZx29J6MKnMRZtQ1m98yu8kziNwHnSPolSXfJmohYls5nsZekPYDngWl4ciGzbtmaXoLPO/843jiNenH0efFJBt30L/S54vy8Q+mel5/tsV1VLYlLuhY4BhghaQnwdaAZICJ+CswETgCeADYAZ6Trtko6B/gj0Bf4eUQsrFacZo1kyIBm+vQpbhJnzWOw/CF487tgwNC8o+m60ftB0wDgZ93eVdWSeEScWmF9AGd3sG4mSZI3sx4weOMy/q3p1+jP97b/q1NRPPL75Pm4/w07759vLD2iFydxs3qwYfNWHlm2Lu8wum3o49fzv5puJP7eDAXuTqFlMwwaBYN3zjuSXsNJ3KyMC373ML+897nKG/Zyn+27Hpohvroc9e2bdzjWg5zEzcpYt3EruwwdwLc/cGDeoXTL7vPvgfnQp8hX4dYuJ3GzMo5c83v+fcuv2P3WHfIOpXtWPZZ3BFYlTuJmZey74T5GbFsNow/LO5TuGb0fjNgb+nji0nrjJG5Wwao+Ixj0wcvyDsOsXf7fsplZgTmJm5kVmJO4mVmBOYmbmRWYk7iZWYE5iZuZFVhVk3ilgseSviRpXvpYIKlF0pvSdYslzU/XudKDmVk7qjkVbWvB4+NICkDcK+mmiHi4dZuI+B7wvXT7k4DPR0RpXc5jI2JVtWI0Myu6al6Jd7bg8anAtVWMx8ys7mS+Epd0JDC+tE1EXFGmSeaCx5J2ACYD55QsDuBWSQH8LK2l2V7b7YWSx40bV/FzmJnVk0xJXNKVwJuBeUBLujiAckm8MwWPTwLuatOVMikilkoaBdwmaVFEzHrDDtsUSi77QaxmvnPzIv78yPK8w+i2c9dsZHizTyvrvbJeiU8EJqTVeLLqqBBye6bRpislIpamzyskzSDpnnlDErde6sFf8rnN83jToH55R9It7+h7D2sG7pF3GGYdyprEFwA7A8s6se97yVDwWNJQ4J3A6SXLBgF9ImJd+vp44IJOHNtyNm3zdewSK+jXZ3TeoXTPkF0Zuu8/5h2FWYeyJvERwMOS5gCbWhdGxMkdNeio4LGkT6Xrf5pu+n7g1oh4paT5aGBGWpW7CbgmIm7JGKv1EgsGHcGhn/9d3mGY1bWsSfwbXdl5ewWPS5J36/vLgMvaLHsKOKgrxzQzaySZknhE/E3SaKB1Zvw5EbGiemGZmVkWmcaJS/oQMAf4IPAh4B5Jp1QzMDMzqyxrd8pXgcNar74ljQT+BFxXrcDMzKyyrHds9mnTfbK6E23NzKxKsl6J3yLpj7w2lvufafODpZmZ1V7WHza/JOkDwCSSOzGnR8SMqkZmZmYVZZ47JSKuB66vYixmZtZJZZO4pDsj4ihJ63j9vCcCIiKGVDU6MzMrq2wSj4ij0ufBtQnHzMw6I+s48SuzLDMzs9rKOkxwv9I3kpqAt/V8OGZm1hllk7ikr6T94QdKWps+1gHLgd/WJEIzM+tQ2SQeEd8GhgJXRMSQ9DE4IoZHxFdqE6KZmXWkYndKRGyjizMKZqh2f4ykNSUV78/P2tbMzLKPE58t6bCIuDfrjrNUu0/dEREndrGtmVlDy/rD5rHA3yU9KekhSfMlPVShTWer3fdUWzOzhpH1SnxKF/adtdr9OyQ9SFJ/84sRsbATbV3t3swaWqYr8Yh4BhhGUpX+JGBYuqycLNXu7wd2j4iDgB8CN3aibWts0yNiYkRMHDlyZIWQzMzqS9abfc4FrgZGpY+rJH2mQrOK1e4jYm1ErE9fzwSaJY3I0tbMzLJ3p3wCOKK1mLGk7wJ/J7l67kjFaveSdgaWR0RIOpzkfyqrgZcrtTUzs+xJXEBLyfsW2u/y2C5jtftTgE9L2gq8CkyLiADabduJz2Vm1hCyJvFfkNTVnEGSvKcCl1ZqVKnafUT8CPhR1rZmZvZ6WYtCXCjpduCodNEZEfFA1aIyM7NMOlsnUySjRMp2pZiZWW1kHZ1yPnA5sBMwAviFpK9VMzAzM6ssa5/4qcAhEbERQNJ3SMZ4f6tagZmZWWVZk/hiYACwMX3fH3iyGgE1tAi46p9g5WN5R9Jt47ct4UX2yDsMs7qXNYlvAhZKuo2kT/w44E5JFwFExGerFF/jefIvsPMBsHOXJo7sNW5ZuIxFQ0/g0LwDMatzWZP4jPTR6vaeD8Vavbjb8bx0+BfyDqNbPjf3Dt61w6i8wzCre1mHGF4uqR+wd7ro0YjYUr2wGlNEIODyuxfzgzv+lnc43da/qW/eIZjVvUxJXNIxJKNTFpMML9xN0sciYlbVImtge43ekR8cfXDeYXTb2/ccnncIZnUva3fK/wDHR8SjAJL2Bq7FxZKrYviO/XnHwWPzDsPMCiDrzT7NrQkcICIeA5qrE5KZmWWV9Up8rqRLgSvT96cBc6sTkpmZZZU1iX8KOBv4LEmf+Czg4moFZWZm2VRM4pL6AHMjYn/gws7sXNJk4Ack08leEhHfabP+NODL6dv1wKcj4sF03WJgHcm0t1sjYmJnjm1m1ggqJvGI2CbpQUnjIuLZrDvOWLH+aeCdEfGSpCnAdF5fS/PYiFiV9ZhmZo0ma3fKGJI7NucAr7QujIiTy7TZXrEeQFJrxfrtSTwi7i7ZfjZJGTYzM8soaxL/Zhf2nblifeoTwM0l7wO4VVIAP4uI6e01crV7M2tkZZO4pAEkP2q+BZgPXBoRWzPuO3PFeknHkiTxo0oWT4qIpZJGAbdJWtTezUVpcp8OMHHixHb3b2ZWryqNE78cmEiSwKeQ3PSTVaaK9ZIOBC4BpkbE6tblEbE0fV5BMm/L4Z04tplZQ6jUnTIhIg4ASMeJz+nEvrNUux8H3AB8JL2BqHX5IKBPRKxLXx8PXNCJY5uZNYRKSXz7JFdp9frMO85Y7f58YDhwcbrv1qGEo4EZ6bIm4JqIuCXzwc3MGkSlJH6QpLXpawED0/cCIiKGlGucodr9mcCZ7bR7Cij2hNpmZjVQNolHhOcSNTPrxTpb7d7MzHoRJ3EzswLLerNPMbz4FFwzLe8oumFb3gGYWcHUVxLf8iqsfT7vKLoheHDbnjw/xOWFzSyb+kriQ8fCp+7IO4qui2DqV2byuWF75R2JmRWE+8TNzArMSdzMrMCcxM3MCqyu+sRf2rCZ/5z5SN5hdFmEJ2E0s86pqyS+7tWtXPn3Z/IOo8uCoH9TH946enDeoZhZQdRVEh8xuD8P/+/JeYdhZlYzVe0TlzRZ0qOSnpB0XjvrJemidP1Dkg7N2tbMzKqYxEsKJU8BJgCnSprQZrMpwF7p4yzgJ51oa2bW8Kp5Jb69UHJEbAZaCyWXmgpcEYnZwDBJYzK2NTNreNXsE89SKLm9bcZmbAu8vlAysEnSgm7E3FuMAFblHUQP8OfoXfw5ep+3dncH1UziWQold7RN5iLLpYWSJd2XVgYqNH+O3sWfo3epl88ByWfp7j6qmcSzFEruaJt+GdqamTW8avaJby+ULKkfSaHkm9pscxPw0XSUytuBNRGxLGNbM7OGV7Ur8YyFkmcCJwBPABuAM8q1zXDY6T3/SXLhz9G7+HP0LvXyOaAHPot8q7eZWXF5AiwzswJzEjczK7C6SOL1cou+pJ9LWlHkse6SdpP0V0mPSFoo6dy8Y+oqSQMkzZH0YPpZvpl3TN0hqa+kByT9Pu9YukrSYknzJc3rieF5eZE0TNJ1khal/1be0eV9Fb1PPL1F/zHgOJIhi/cCp0bEw7kG1gWSjgbWk9zFun/e8XRFesftmIi4X9JgYC7wvoL+9xAwKCLWS2oG7gTOTe8uLhxJXwAmAkMi4sS84+kKSYuBiRFR6Jt9JF0O3BERl6Qj8HaIiJe7sq96uBKvm1v0I2IW8GLecXRHRCyLiPvT1+uAR0juwC2cdDqI9enb5vRRyKseSbsC7wUuyTuWRidpCHA0cClARGzuagKH+kjiHd26bzmTNB44BLgn51C6LO2CmAesAG6LiKJ+lu8D/w5syzmO7grgVklz0yk3imhPYCXwi7R76xJJg7q6s3pI4plv0bfakbQjcD3wuYhYm3c8XRURLRFxMMldw4dLKlw3l6QTgRURMTfvWHrApIg4lGSG07PTLsiiaQIOBX4SEYcArwBd/i2vHpJ4ltv7rYbS/uPrgasj4oa84+kJ6Z+7twNFrDoyCTg57U/+JfAuSVflG1LXRMTS9HkFMIOkO7VolgBLSv6qu44kqXdJPSRx36Lfi6Q/Bl4KPBIRF+YdT3dIGilpWPp6IPAeYFGuQXVBRHwlInaNiPEk/z7+EhGn5xxWp0kalP5YTtr9cDxQuJFcEfEC8Jyk1hkM3w10+Yf/wpdn68Yt+r2OpGuBY4ARkpYAX4+IS/ONqtMmAR8B5qd9yQD/EREz8wupy8YAl6cjoPoAv46Iwg7PqwOjgRnJdQJNwDURcUu+IXXZZ4Cr0wvPp0inHOmKwg8xNDNrZPXQnWJm1rCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzArMSdwakqTFkl6VtF7SS5L+IGm3dN1lkr7VQbuQ9ErabrWkP0v659pGb/YaJ3FrZCdFxI7AGGA58MOM7Q5K270VuAz4kaSvVydEs/KcxK3hRcRG4DpgQifbrYqIK4FPA1+RNLwa8ZmV4yRuDU/SDsA/A7O7uIvfAk3A4T0WlFlGTXkHYJajGyVtBXYEVgD/2JWdRMQWSauAN/VkcGZZ+ErcGtn7ImIY0B84B/ibpJ07uxNJzcBI4MWeDc+sMidxa3gR0RIRNwAtwFFd2MVUYCswp0cDM8vASdwanhJTgZ2AR9LFfSUNKHn0a6fdmySdBvwY+G5ErK5h2GYAKCLyjsGs5iQtBkaTXH0H8Azw7Yi4WtJlwMfaNLkrIo6SFMCGtM1m4EHg/0XENbWK3ayUk7iZWYG5O8XMrMCqlsQl/VzSCkkLOlgvSRdJekLSQ5IOLVk3WdKj6brzqhWjmVnRVfNK/DJgcpn1U4C90sdZwE8AJPUl+aFoCskddKdK6tSddGZmjaJqSTwiZlF+3OxU4IpIzAaGSRpDctfbExHxVERsBn6ZbmtmZm3kecfmWOC5kvdL0mXtLT+io51IOovkSp5Bgwa9bZ999un5SM2sbqxbvYxBm1bkGkMftgEwd9m2VRExsjv7yjOJq51lUWZ5uyJiOjAdYOLEiXHffff1THRmVpfuuegjHLD6jzyzR74zCKtpAPt+5L+f6e5+8kziS4DdSt7vCiwF+nWw3MysR2zQQPb9eNaZh6voI//d7V3kmcRvAs6R9EuS7pI1EbFM0kpgL0l7AM8D04AP5xinmfWQ1es38dKGzbnGsPbVLbkev6dVLYlLuhY4BhghaQnwdaAZICJ+CswETgCeILkD7ox03VZJ5wB/BPoCP4+IhdWK08xqY/PWbRz9X3/llc0tucbxn02bUFN7vbbFVLUkHhGnVlgfwNkdrJtJkuTNrE5s3bqF6/gSe+ywkqY++SXRpq0b2DqwW78l9iqeT9ysAWzeuo35z79My7b8Yti4YS1H93mWZYMPYcyESfkFAjSNPbTyRgXhJG7WAP7yuyvZf94F9CW/LD6QTSBYuvOxjPlHlyTtKU7iZg1g6EsL2VWreOHNp6CcpkzaBCzv25cD3n16LsevV07iZlW2cUtL7iMy1m9KRmSM+vB0+vTtm2ss1rOcxM2q7ISL7uCpla/kGsNn+67luOZcQ7AqcRI3q7KV6zYx6S3DOenAXXKLYb/H74THoY/qZ2idJZzEra5tadnGtpwLn2xp2cbeowcz7fBx+QWxYRg8nt/hrXqcxK1u3fXEKj768zm0bMs3iZ/R92bev3gl3DAsvyAe+mV+x7aqchK3urXkpQ20bAs++c49GTIgvw7hM+88i+a1QMvw3GJg6DgY+VZwd0rdcRK3qrj7iVXc98xLucbwm7nJjMYfe8d4dhk2ML9AZveFgz8MU76TXwxWt5zErSou+P3DLHphXd5hMHxQP4YO9LAMq19O4nXmhTUbOfeXD/DqlnwnGVr0wjqm7L8zP/pwvrc3C+iT4zwdZtXmJF5nFr2wlnuefpFDxw3L9Qr02LeO5P2HjKWvE6hZVVU1iUuaDPyAZErZSyLiO23Wfwk4rSSWfYGREfGipMXAOqAF2BoRE6sZa7352okTOHTcTnmHka/n5sCvToeWfO+WZNMa/6BoVVPN+cRbq9YfR1LF515JN0XEw63bRMT3gO+l258EfD4iSosrHxsRq6oVY097ZNla7noi33D/9tjKXI/fq6xcBOuXw0Efhv475hiI4JCP5Hh8q2eZk7ikI4HxpW0i4ooyTbZXrU/bt1atf7iD7U8Frs0aT2/03VsWcfuj+SfRfk19GLlj/3yD+MO/waI/5BvDumXJ87u+BkPH5huLWZVkSuKSrgTeDMwj6d6ApHhxuSSeuWq9pB2AycA5JYsDuFVSAD9LCyK313Z7tftx43K8Iw5o2RYctOtQrjqz3Y9ZM819+zCgOedJjhbfCX2bYc9j8o1jx9EwJL/b3c2qLeuV+ERgQlqNJ6vOVK0/CbirTVfKpIhYKmkUcJukRREx6w07bFPtvhPxVUXfPmJwjjeW0LIFFlwHW/KdcImVi2DCVDi5FxSjNatjWZP4AmBnYFkn9t1RNfv2TKNNV0pELE2fV0iaQdI984Ykbm08czfMOCvvKBKDx+QdgVndy5rERwAPS5pDMrc7ABFxcpk295Khar2kocA7gdNLlg0C+kTEuvT18cAFGWNtbC1pJe9TfwW7HJxrKAwale/xzRpA1iT+jc7uuKOq9ZI+la7/abrp+4FbI6L07//RwAwlw7KagGsi4pbOxtDQdhgOg3fOOwozq7JMSTwi/iZpNHBYumhORKzI0O4NVetLknfr+8uAy9osewo4KEtsZmaNLFOxPUkfAuYAHwQ+BNwj6ZRqBmZmZpVl7U75KnBY69W3pJHAn4DrqhVYER2/bgb/sO5muHhQfkGsWJjfsc2s5rIm8T5tuk9Wk/EqvpEc/Oo9DN+2Ct60f35BvGkP6D8ERu2bXwxmVjNZk/gtkv7Ia8MA/5k2fd2WeL7vbuwz7eq8wzCzBpH1h80vSfoAMInkJp7pETGjqpGZmVlFmedOiYjrgeurGIuZmXVS2SQu6c6IOErSOl5/y7yAiIghVY3OzMzKKpvEI+Ko9HlwbcIxM7POyDpO/Mosy8zMrLayDhPcr/SNpCbgbT0fjpmZdUbZJC7pK2l/+IGS1qaPdcBy4Lc1idDMzDpUNolHxLeBocAVETEkfQyOiOER8ZXahGhmZh2p2J0SEdvwZFRmZr1S1j7x2ZIOq7zZ60maLOlRSU9IOq+d9cdIWiNpXvo4P2tbMzPLfrPPscAnJT0DvMJr48QP7KhBlmr3qTsi4sQutjUza2hZk/iULuy7s9Xue6qtmVnDyNSdEhHPAMNIChqfBAxLl5XTXrX7se1s9w5JD0q6WVLrUMasbZF0lqT7JN23cuXKyh/GzKyOZL3Z51zgamBU+rhK0mcqNWtnWdtq9PcDu0fEQcAPgRs70TZZGDE9IiZGxMSRI0dWCMnMrL5k7U75BHBEax1MSd8F/k6SeDtSsdp9RKwteT1T0sWSRmRpa2Zm2UenCGgped9C+1fLpbZXu5fUj6Ta/U2v26m0s9JqyJIOT+NZnaWtmZllvxL/BUldzRkkyXsqcGm5Bhmr3Z8CfFrSVuBVYFpEBNBu285/PDOz+pa1KMSFkm4HjkoXnRERD2RoV7bafUT8CPhR1rZmZvZ6na2TKZIfGCt1pZiZWQ1kHZ1yPnA5sBMwAviFpK9VMzAzM6ssa5/4qcAhEbERQNJ3SIYHfqtagZmZWWVZu1MWAwNK3vcHnuzxaMzMrFOyXolvAhZKuo2kT/w44E5JFwFExGerFJ+ZmZWRNYnPSB+tbu/5UMzMrLOyDjG8PL3pZu900aMRsaV6YZmZWRaZkrikY0hGpywmGV64m6SPRcSsqkVmZmYVZe1O+R/g+Ih4FEDS3sC1uFiymVmuso5OaW5N4AAR8RjQXJ2QzMwsq6xX4nMlXQpcmb4/DZhbnZDMzCyrrEn8U8DZwGdJ+sRnARdXKygzM8umYhKX1AeYGxH7Axd2ZueSJgM/IJmJ8JKI+E6b9acBX07frgc+HREPpusWA+tIpr3dGhETO3NsM7NGUDGJR8S2tHzauIh4NuuOMxY7fhp4Z0S8JGkKMB04omT9sRGxKusxzcwaTdbulDEkd2zOIal2D0BEnFymTcVixxFxd8n2s0kq+JiZWUZZk/g3u7Dv9oodH9HBtpCUgLu55H0At0oK4GcRMb29RpLOAs4CGDduXBfCNDMrrrJJXNIAkh813wLMBy6NiK0Z95252LGkY0mS+FEliydFxFJJo4DbJC1q7+aiNLlPB5g4cWK7+zczq1eVxolfDkwkSeBTSG76ySpTsWNJBwKXAFMjYnXr8ohYmj6vIJm35fBOHNvMrCFU6k6ZEBEHAKTjxOd0Yt/bix0Dz5MUO/5w6QaSxgE3AB9JbyBqXT4I6BMR69LXxwMXdOLYZmYNoVIS3z7JVVr4OPOOMxZKPh8YDlyc7rt1KOFoYEa6rAm4JiJuyXxwM7MGUSmJHyRpbfpawMD0vYCIiCHlGmcolHwmcGY77Z4CDqocvplZYyubxCOib60CMTOzzutstXszM+tFnMTNzArMSdzMrMCcxM3MCsxJ3MyswJzEzcwKzEnczKzAnMTNzAos61S0hfD8y6/ytRvn53b84zdsYVT/3A5vZg2orpL4yxu2cPP8F3I7/nv7ih0H1NVXama9XF1lnF13Gsjc/++4/AK44sew+ZXK25mZ9ZC6SuKD1z4OP8yxnvLqx2FXT3tuZrVT1SSeodq90vUnABuAj0fE/VnatifUF3Y+oGc/RGfsfADs8978jm9mDadqSTxjtfspwF7p4wjgJ8ARGdu+waYBI+CDv+j5D2Nm1ktVc4jh9mr3EbEZaK12X2oqcEUkZgPDJI3J2NbMrOFVszslS7X79rYZm7Et8Ppq98AmSQu6EXNPGAGscgxA74ijN8QAvSOO3hAD9I44ekMMAG/t7g6qmcSzVLvvaJssbZOFJdXuJd2XlnfLjWPoXXH0hhh6Sxy9IYbeEkdviKE1ju7uo5pJPEu1+4626ZehrZlZw6tmn/j2aveS+pFUu7+pzTY3AR9V4u3AmohYlrGtmVnDq9qVeMZq9zNJhhc+QTLE8IxybTMcdnrPf5JOcwyv6Q1x9IYYoHfE0RtigN4RR2+IAXogDkW029VsZmYF4FkMzcwKzEnczKzA6iKJS/q5pBV5jhGXtJukv0p6RNJCSefmEMMASXMkPZjG8M1ax1ASS19JD0j6fY4xLJY0X9K8nhjK1cUYhkm6TtKi9Nx4Rw4xvDX9DlofayV9Loc4Pp+elwskXStpQK1jSOM4N41hYa2+h/ZylKQ3SbpN0uPp805d2XddJHHgMmByzjFsBf4tIvYF3g6cLWlCjWPYBLwrIg4CDgYmp6N+8nAu8EhOxy51bEQcnOOY4B8At0TEPsBB5PCdRMSj6XdwMPA2kkEEM2oZg6SxwGeBiRGxP8mAhWm1jCGNY3/gX0juCj8IOFHSXjU49GW8MUedB/w5IvYC/py+77S6SOIRMQt4MecYlrVO3hUR60j+sY6tcQwREevTt83po+a/XEvaFXgvcEmtj92bSBoCHA1cChARmyPi5VyDgncDT0bEMzkcuwkYKKkJ2IF87v3YF5gdERsiYivwN+D91T5oBzlqKnB5+vpy4H1d2XddJPHeRtJ44BDgnhyO3VfSPGAFcFtE1DwG4PvAvwPbcjh2qQBulTQ3nZ6h1vYEVgK/SLuWLpE0KIc4Sk0Drq31QSPieeC/gWeBZST3hNxa6ziABcDRkoZL2oFkiPNuFdpUy+j0vhjS51Fd2YmTeA+TtCNwPfC5iFhb6+NHREv6Z/OuwOHpn481I+lEYEVEzK3lcTswKSIOJZkt82xJR9f4+E3AocBPIuIQ4BW6+CdzT0hvnDsZ+E0Ox96J5MpzD2AXYJCk02sdR0Q8AnwXuA24BXiQpCu0sJzEe5CkZpIEfnVE3JBnLOmf7bdT+98KJgEnS1pMMvvkuyRdVeMYAIiIpenzCpI+4FpX7FgCLCn5a+g6kqSelynA/RGxPIdjvwd4OiJWRsQW4AbgyBziICIujYhDI+Joki6Ox/OIA1ieztpK+ryiKztxEu8haYGLS4FHIuLCnGIYKWlY+nogyT+cRbWMISK+EhG7RsR4kj/d/xIRNb/ikjRI0uDW18DxJH9K10xEvAA8J6l1prp3A2XnxK+yU8mhKyX1LPB2STuk/1beTU4/fEsalT6PA/6J/L6Tm4CPpa8/Bvy2Kzupi/Jskq4FjgFGSFoCfD0iLq1xGJOAjwDz0z5pgP+IiJk1jGEMcHlaVKMP8OuIyG2IX85GAzOSfEETcE1E3JJDHJ8Brk67Mp4inVqi1tL+3+OAT+Zx/Ii4R9J1wP0k3RcPkN+t79dLGg5sAc6OiJeqfcD2chTwHeDXkj5B8j+5D3Zp377t3sysuNydYmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmBO4mZmBeYkbmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmBO4mZmBeYkbmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmBO4mZmBeYkbmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmBO4mZmBeYkbmZWYE7iZmYF5iRuZlZgTuJmZgXmJG5mVmBO4mZmBeYkbnVH0u2SXpLUv83yD0u6T9J6Scsk3SzpqJL1e0v6jaRVktZIekjSFyT1lTReUqRtSx//nLbdVdL1JW3nS/p4yb4/IWmRpHWSlkv6g6TBNftSrG45iVtdkTQe+AcggJNLln8B+D7wn8BoYBxwMTA1Xf9m4B7gOeCAiBgKfBCYCJQm22ERsWPJ41fp8ivTtrsDw4GPAsvTfb8zPe6pETEY2Bf4dU9/dmtMioi8YzDrMZLOB/6RJCHvHREnShoKPA+cERG/6aDdVcBOEfHeDtaPB54GmiNiazvr1wNHRcS8dtZ9MV33vi59KLMyfCVu9eajwNXp4x8ljQbeAQwAZpRp9x7gum4cdzbwY0nTJI1rs+6eNJZvSprUtpvHrDucxK1upP3buwO/joi5wJPAh0m6N1a1dwVdYjiwLMNhVkl6ueSxb7r8g8AdwP8HPC1pnqTDACLiDuCfgEOBPwCrJV0oqW8XPqbZ6ziJWz35GHBrRKxK31+TLlsNjJDUVKbtamBMhmOMiIhhJY9HACLipYg4LyL2I+lznwfcKEnp+psj4iTgTST98B8Hzuz0JzRrw33iVhckDQReAPoC69PF/YFhwMHAXcDHI6LdLpO0T3xYRJzYwfrxlOkTb2f7/YH5JEl/dTvrrwOWRcRnKu3LrBxfiVu9eB/QAkwgSdoHk4wCuYOkn/x8kj7r90naQVKzpCmS/itt/3XgSEnfk7QzgKS3SLpK0rBKB5f0XUn7S2pKhw5+GngiIlZLmpr2le+kxOHAO0n60c26xUnc6sXHgF9ExLMR8ULrA/gRcBpwEfAF4GvASpLhgOcANwJExJMkP4COBxZKWgNcD9wHrCs5zsttxol/IV2+A8kPpy8DT5H0zbcOcXwJ+BfgcWAtcBXwvYi4uqe/BGs87k4xMyswX4mbmRVY1ZK4pJ9LWiFpQQfrJekiSU+ktzcfWrJusqRH03XnVStGM7Oiq+aV+GXA5DLrpwB7pY+zgJ8ApGNnf5yunwCcKmlCFeM0MyusqiXxiJgFvFhmk6nAFZGYDQyTNAY4nORX/aciYjPwy3RbMzNro9zND9U2lmSEQKsl6bL2lh/R0U4knUVyJc+gQYPets8++/R8pGZmPWjzplfpt3oRc5dtWxURI7uzrzyTuNpZFmWWtysipgPTASZOnBj33Xdfz0RnZlYlzz3+ILtdfTT65tpnuruvPJP4EmC3kve7AkuBfh0sNzOzNvIcYngT8NF0lMrbgTURsQy4F9hL0h6S+gHT0m3NzKyNql2JS7oWOIZk4qElJLc1NwNExE+BmcAJwBPABuCMdN1WSecAfySZB+PnEbGwWnGamRVZ1ZJ4RJxaYX0AZ3ewbiZJkjczszJ8x6aZWYHl+cOmWe/33L2w8Ia8o7A6M3zZoh7bl5O4WTmzfwwLb4T+LkxvPWdAy2ZWxhCSSS27x0ncrIw1GzazZeDu/OLgX1Xe2Cyjp1e9wsz5LwDt1iDpFCdxszKeXLmewa9s4Wd/eyrvUKyObN0W7NCvZ0qs1lUSD2BLy7a8w7A6si2gX5N44oIT8g7F6pD+d/f3UVdJfMHza9jrqzfnHYbVkR81b2Rkc3szQZj1DnWVxIcObOaLx++ddxhWR9768GBGbumfdxhmHaq7JH7Ou/bKOwyrJ6t2hBU903dpVg2+2cfMrMDq6kp80PrFcOX78w7D6smTf4ER7qKz3quuknifbVtg07q8w7B6MnYi7P2PeUdh1qG6SuIbB46GM/+UdxhmZjVT1T7xSlXrJX1J0rz0sUBSi6Q3pesWS5qfrnO5HjOzdlRzPvHWqvXHkVTxuVfSTRHxcOs2EfE94Hvp9icBn4+I0uLKx0bEqmrFaGZWdJmTuKQjgfGlbSLiijJNtletT9u3Vq1/uIPtTwWuzRqPmZllTOKSrgTeDMwDWtLFAZRL4pmr1kvaAZgMnFOyOIBbJQXws7Qgcnttt1e7Hz92VKWPYmZWV7JeiU8EJqTVeLLqTNX6k4C72nSlTIqIpZJGAbdJWhQRs96ww5Jq9xP23qMz8ZmZFV7WHzYXADt3ct8dVbNvzzTadKVExNL0eQUwg6R7xszMSmS9Eh8BPCxpDrCpdWFEnFymzfaq9cDzJIn6w203kjQUeCdwesmyQUCfiFiXvj4euCBjrGZmDSNrEv9GZ3fcUdV6SZ9K1/803fT9wK0R8UpJ89HADEmtMV4TEbd0NgYzs3qnrN3ckkYDh6Vv56TdHL3KhL33iIcfezrvMMzMMpE0NyImdmcfmfrEJX0ImAN8EPgQcI+kU7pzYDMz676s3SlfBQ5rvfqWNBL4E3BdtQIzM7PKso5O6dOm+2R1J9qamVmVZL0Sv0XSH3ltGOA/AzOrE5KZmWWVKYlHxJckfQCYRHITz/SImFHVyMzMrKLMc6dExPXA9VWMxczMOqlsEpd0Z0QcJWkdr79lXkBExJCqRmdmZmWVTeIRcVT6PLg24ZiZWWdkHSd+ZZZlZmZWW1mHCe5X+kZSE/C2ng/HzMw6o2wSl/SVtD/8QElr08c6YDnw25pEaGZmHSqbxCPi28BQ4IqIGJI+BkfE8Ij4Sm1CNDOzjlTsTomIbcBBXdl5hkLJx0haU1Is+fysbc3MLPs48dmSDouIe7PuOEuh5NQdEXFiF9uamTW0rD9sHgv8XdKTkh6SNF/SQxXabC+UHBGbgdZCyVl0p62ZWcPIeiU+pQv7zloo+R2SHiQp3fbFiFjYibYulGxmDS3TlXhEPAMMIylofBIwLF1WTpZCyfcDu0fEQcAPgRs70bY1tukRMTEiJg7cYYcKIZmZ1ZesN/ucC1wNjEofV0n6TIVmFQslR8TaiFifvp4JNEsakaWtmZll7075BHBEax1MSd8F/k5y9dyRioWSJe0MLI+IkHQ4yf9UVgMvV2prZmbZk7iAlpL3LbTf5bFdxkLJpwCflrQVeBWYFknRz3bbduJzmZk1hEyFkiV9AfgYMIMkeU8FLouI71c1uk5yoWQzK5KeKJSctSjEhZJuB45KF50REQ9058C1smXLFpYsWcLGjRvzDqWmBgwYwK677kpzc3PeoZhZFWUuCpESsI0KXSm9yZIlSxg8eDDjx49HKkzY3RIRrF69miVLlrDHHnvkHY6ZVVHW0SnnA5cDOwEjgF9I+lo1A+spGzduZPjw4Q2TwAEkMXz48Ib768OsEWW9Ej8VOCQiNgJI+g7JGO9vVSuwntRICbxVI35ms0aU9bb7xcCAkvf9gSd7PBozM+uUrFfim4CFkm4juXPyOOBOSRcBRMRnqxSfmZmVkTWJz0gfrW7v+VDMzKyzsg4xvFxSP2DvdNGjEbGlemFVxzd/t5CHl67t0X1O2GUIXz9pvw7XL168mMmTJ3PEEUfwwAMPsPfee3PFFVcwYcIE7rvvPkaMGMF9993HF7/4RW6//Xa+8Y1v8PTTT7Ns2TIee+wxLrzwQmbPns3NN9/M2LFj+d3vfkdzczPnnXceN910E01NTRx//PH893//d49+LjMrhqyjU44BHieZ4/ti4DFJR1cvrPry6KOPctZZZ/HQQw8xZMgQLr744rLbP/nkk/zhD3/gt7/9LaeffjrHHnss8+fPZ+DAgfzhD3/gxRdfZMaMGSxcuJCHHnqIr32tEAOFzKwKsnan/A9wfEQ8CiBpb+BaClYsudwVczXttttuTJo0CYDTTz+diy66qOz2U6ZMobm5mQMOOICWlhYmT54MwAEHHMDixYs58cQTGTBgAGeeeSbvfe97OfHEE8vuz8zqV9bRKc2tCRwgIh4DfCtgRm2H+0miqamJbdu2AbxhPHf//v0B6NOnD83Nzdvb9+nTh61bt9LU1MScOXP4wAc+wI033rg9yZtZ48maxOdKujStiXmMpP8HzK1mYPXk2Wef5e9//zsA1157LUcddRTjx49n7tzkK7z++us7tb/169ezZs0aTjjhBL7//e8zb968ng7ZzAoia3fKp4Czgc+S3HI/i6Rv3DLYd999ufzyy/nkJz/JXnvtxac//WkOP/xwPvGJT/Cf//mfHHFEu0WLOrRu3TqmTp3Kxo0biQj+7//9v1WK3Mx6u4qzGErqAzwUEft3eufSZOAHJNPJXhIR32mz/jTgy+nb9cCnI+LBdN1iYB3JtLdbs8z01d4sho888gj77rtvZ0PvMa192AsWLKj5sfP+7GZWXk1mMYyIbZIelDQuIp7tRHBZKtY/DbwzIl6SNAWYzutraR4bEauyHtPMrNFk7U4ZQ3LH5hzgldaFEXFymTbbK9YDSGqtWL89iUfE3SXbzyYpw1ZXxo8fn8tVuJk1hqxJ/Jtd2HfmivWpTwA3l7wP4FZJAfwsIqa318jV7s2skZVN4pIGkPyo+RZgPnBpRGzNuO/MFeslHUuSxI8qWTwpIpZKGgXcJmlRRMx6ww6T5D4dkj7xjLGZmdWFSkMMLwcmkiTwKSQ3/WSVqWK9pAOBS4CpEbG6dXlELE2fV5DM23J4J45tZtYQKnWnTIiIAwAkXQrM6cS+s1S7HwfcAHwkvYGodfkgoE9ErEtfHw9c0Iljm5k1hEpJfPskV2n1+sw7zljt/nxgOHBxuu/WoYSjgRnpsibgmoi4JfPBzcwaRKUkfpCk1mn/BAxM3wuIiBhSrnFEzARmtln205LXZwJnttPuKeCgyuGbmTW2skk8IvrWKpCauPk8eGF+z+5z5wNgync6XP3lL3+Z3XffnX/9138F4Bvf+AaSmDVrFi+99BJbtmzhW9/6FlOnTgXgqquu4qKLLmLz5s0cccQRXHzxxfTtW1//Gcys52SdO8W6aNq0afzqV7/a/v7Xv/41Z5xxBjNmzOD+++/nr3/9K//2b/9GRPDII4/wq1/9irvuuot58+bRt29frr766hyjN7PeLus48fpQ5oq5Wg455BBWrFjB0qVLWblyJTvttBNjxozh85//PLNmzaJPnz48//zzLF++nD//+c/MnTuXww47DIBXX32VUaM89t3MOtZYSTwnp5xyCtdddx0vvPAC06ZN4+qrr2blypXMnTuX5uZmxo8fv30yq4997GN8+9vfzjtkMysId6fUwLRp0/jlL3/JddddxymnnMKaNWsYNWoUzc3N/PWvf+WZZ54B4N3vfjfXXXcdK1asAODFF1/cvs7MrD2+Eq+B/fbbj3Xr1jF27FjGjBnDaaedxkknncTEiRM5+OCD2WeffQCYMGEC3/rWtzj++OPZtm0bzc3N/PjHP2b33XfP+ROYWW9VcSraIumNU9HmqZE/u1kR9MRUtO5OMTMrMCdxM7MCa4gkXk9dRlk14mc2a0R1n8QHDBjA6tWrGyqpRQSrV69mwIABeYdiZlVW96NTdt11V5YsWcLKlSvzDqWmBgwYwK671l2hJDNro+6TeHNzM3vssUfeYZiZVUVVu1MkTZb0qKQnJJ3XznpJuihd/5CkQ7O2NTOzKibxkmr3U4AJwKmSJrTZbAqwV/o4C/hJJ9qamTW8al6Jb692HxGbgdZq96WmAldEYjYwTNKYjG3NzBpeNfvEs1S7b2+bsRnbAq+vdg9skrSgGzFXwwhgVd5BtKM3xtUbYwLH1Rm9MSbovXG9tbs7qGYSz1LtvqNtsrRNFpZUu5d0X3dvYe1pvTEm6J1x9caYwHF1Rm+MCXp3XN3dRzWTeJZq9x1t0y9DWzOzhlfNPvHt1e4l9SOpdn9Tm21uAj6ajlJ5O7AmIpZlbGtm1vCqdiWesdr9TOAE4AlgA3BGubYZDju95z9Jt/XGmKB3xtUbYwLH1Rm9MSao47jqaipaM7NGU/dzp5iZ1TMncTOzAitEEpf0c0krOhoDnsft+xliOi2N5SFJd0s6qGTdYknzJc3riSFGnYzrGElr0mPPk3R+ybq8vqsvlcSzQFKLpDel66r5Xe0m6a+SHpG0UNK57WxT03MrY0w1P7cyxpXHuZUlrpqeX5IGSJoj6cE0pm+2s03PnVcR0esfwNHAocCCDtafANxMMr787cA96fK+wJPAniTDFh8EJtQopiOBndLXU1pjSt8vBkbk9F0dA/y+neW5fVdttj0J+EuNvqsxwKHp68HAY20/c63PrYwx1fzcyhhXHudWxbhqfX6l58qO6etm4B7g7dU6rwpxJR4Rs4AXy2xS89v3K8UUEXdHxEvp29kkY92rLsN31ZHcvqs2TgWu7YnjVhIRyyLi/vT1OuARkruFS9X03MoSUx7nVsbvqiPVPLc6G1fVz6/0XFmfvm1OH21HkPTYeVWIJJ5BZ27fz3ri9aRPkPxft1UAt0qaq2TagFp7R/qn3s2S9kuX5f5dSdoBmAxcX7K4Jt+VpPHAISRXTaVyO7fKxFSq5udWhbhyO7cqfV+1PL8k9ZU0D1gB3BYRVTuv6mU+8W7fvl8tko4l+Yd2VMniSRGxVNIo4DZJi9Kr1Vq4H9g9ItZLOgG4kWQWydy/K5I/de+KiNKr9qp/V5J2JPmH/bmIWNt2dTtNqn5uVYipdZuan1sV4srt3MryfVHD8ysiWoCDJQ0DZkjaPyJKfxPqsfOqXq7EO7p9P8ut/1Uj6UDgEmBqRKxuXR4RS9PnFcAMkj+haiIi1rb+qRcRM4FmSSPI+btKTaPNn7rV/q4kNZP84786Im5oZ5Oan1sZYsrl3KoUV17nVpbvK1Xz8ysiXgZuJ/kLoFTPnVc92aFfzQcwno5/rHsvr/+RYE66vAl4CtiD134k2K9GMY0juRP1yDbLBwGDS17fDUyu4Xe1M6/d5HU48Gz6veX2XaXrh5L0mw+q1XeVfu4rgO+X2aam51bGmGp+bmWMq+bnVpa4an1+ASOBYenrgcAdwInVOq8K0Z0i6VqSX75HSFoCfJ3kxwKiOrfv90RM5wPDgYslAWyNZBa10SR/XkHyH+yaiLilJ2LKGNcpwKclbQVeBaZFcvbk+V0BvB+4NSJeKWla1e8KmAR8BJif9l8C/AdJkszr3MoSUx7nVpa4an5uZYwLant+jQEuV1Lcpg/w64j4vao05YhvuzczK7B66RM3M2tITuJmZgXmJG5mVmBO4mZmBeYkbmZWYE7iZmYF5iRuZlZg/z9AiJviBGCjOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate Distributions of All HH variables\n",
    "fig, axes = plt.subplots(nrows=len(VAR_NAMES), ncols=1, figsize=(5,10))\n",
    "fig.tight_layout(h_pad=4)\n",
    "\n",
    "for i, var in enumerate(VAR_NAMES):\n",
    "    sns.ecdfplot(data=pums_data, x=var, ax=axes[i]).set(xlim=(min(pums_data[var]),max(pums_data[var])))\n",
    "    sns.ecdfplot(data=synth_hh_df, x=var, ax=axes[i]).set(xlim=(min(pums_data[var]),max(pums_data[var])))\n",
    "    axes[i].set_title(var)\n",
    "    axes[i].set_xlabel(None)\n",
    "    plt.legend(labels=['pums','vae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Univariate (marginal) SRMSE: 0.2970742924455093, Total Bins: 48\n",
      "Bivariate (joint) SRMSE: 0.9541625445017217, Total Bins: 861\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.874605178833008, 0.2970742924455093, 0.9541625445017217)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testModel(test_vae=vae_model, latent_dim=LATENT_DIM)\n",
    "# gen_time, srmse_uni, srmse_bi"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gensynth] *",
   "language": "python",
   "name": "conda-env-gensynth-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}